From 37d27f9b24808090d6a5127533cd19e113e0a3b8 Mon Sep 17 00:00:00 2001 From: milen <1649759610@qq.com> Date: Fri, 24 Jun 2022 13:43:34 +0000 Subject: [PATCH 001/116] initial commit --- examples/text_matching/diffcse/README.md | 151 +++ .../text_matching/diffcse/custom_ernie.py | 1174 +++++++++++++++++ examples/text_matching/diffcse/data.py | 166 +++ .../text_matching/diffcse/eval_metrics.py | 146 ++ examples/text_matching/diffcse/model.py | 245 ++++ examples/text_matching/diffcse/run_diffcse.py | 376 ++++++ examples/text_matching/diffcse/run_eval.sh | 19 + examples/text_matching/diffcse/run_infer.sh | 20 + examples/text_matching/diffcse/run_train.sh | 28 + examples/text_matching/diffcse/test_train.sh | 25 + examples/text_matching/diffcse/utils.py | 28 + 11 files changed, 2378 insertions(+) create mode 100644 examples/text_matching/diffcse/README.md create mode 100644 examples/text_matching/diffcse/custom_ernie.py create mode 100644 examples/text_matching/diffcse/data.py create mode 100644 examples/text_matching/diffcse/eval_metrics.py create mode 100644 examples/text_matching/diffcse/model.py create mode 100644 examples/text_matching/diffcse/run_diffcse.py create mode 100644 examples/text_matching/diffcse/run_eval.sh create mode 100644 examples/text_matching/diffcse/run_infer.sh create mode 100644 examples/text_matching/diffcse/run_train.sh create mode 100644 examples/text_matching/diffcse/test_train.sh create mode 100644 examples/text_matching/diffcse/utils.py diff --git a/examples/text_matching/diffcse/README.md b/examples/text_matching/diffcse/README.md new file mode 100644 index 000000000000..1662d3554719 --- /dev/null +++ b/examples/text_matching/diffcse/README.md @@ -0,0 +1,151 @@ +# 无监督语义匹配模型 [DiffCSE](https://arxiv.org/pdf/2204.10298.pdf) + +我们借鉴 DiffCSE 论文的关键思想，实现了 DiffCSE 模型。相比于 SimCSE 模型，DiffCSE模型会更关注语句之间的差异性，具有精确的向量表示能力。DiffCSE 模型同样适合缺乏监督数据，但是又有大量无监督数据的匹配和检索场景。 + +## 快速开始 +### 代码结构说明 + +以下是本项目主要代码结构及说明： + +``` +DiffCSE/ +├── model.py # DiffCSE 模型组网代码 +├── custom_ernie.py # 为适配DiffCSE模型，对ERNIE模型进行了部分修改 +├── data.py # 无监督语义匹配训练数据、测试数据的读取逻辑 +├── run_diffcse.py # 模型训练、评估、预测的主脚本 +├── eval_metrics.py # 模型测试用的指标计算脚本，包括 spearman, Precison, Recall 等指标 +├── utils.py # 包括一些常用的工具式函数 +├── run_train.sh # 模型训练的脚本 +├── run_eval.sh # 模型评估的脚本 +└── run_infer.sh # 模型预测的脚本 +``` + +### 模型训练 +可以运行如下命令，开始模型训练并且进行模型测试。 + +```shell +export CUDA_VISIBLE_DEVICES=0,1,2,3 + +python -u -m paddle.distributed.launch --gpus ${gpu_ids} \ + run_diffcse.py \ + --mode "train" \ + --extractor_name "rocketqa-zh-dureader-query-encoder" \ + --generator_name "ernie-1.0" \ + --discriminator_name "ernie-1.0" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --train_set_file ./data/train.txt \ + --eval_set_file ./data/test_v1.txt \ + --save_dir ./checkpoints \ + --save_steps "10000" \ + --eval_steps "1000" \ + --batch_size "32" \ + --epochs "3" \ + --learning_rate "3e-5" \ + --weight_decay "0.01" \ + --warmup_proportion "0.01" \ + --dropout "0.1" \ + --margin "0.0" \ + --scale "20" \ + --seed "0" \ + --device "gpu" +``` + +可支持配置的参数： +* `mode`：可选，用于指明本次运行是模型训练、模型评估还是模型预测，仅支持[train, eval, infer]三种模式；默认为 infer。 +* `extractor_name`：可选，DiffCSE模型中用于向量抽取的模型名称；默认为 ernie-1.0。 +* `generator_name`: 可选，DiffCSE模型中生成器的模型名称；默认为 ernie-3.0-base-zh。 +* `discriminator_name`: 可选，DiffCSE模型中判别器的模型名称；默认为 rocketqa-zh-dureader-query-encoder。 +* `epochs`: 模型训练轮次，默认为3。 +* `batch_size`：可选，批处理大小，请结合显存情况进行调整，若出现显存不足，请适当调低这一参数；默认为32。 +* `learning_rate`：可选，Fine-tune 的最大学习率；默认为5e-5。 +* `weight_decay`：可选，控制正则项力度的参数，用于防止过拟合，默认为0.01。 +* `warmup_proption`：可选，学习率 warmup 策略的比例，如果0.1，则学习率会在前10%训练step的过程中从0慢慢增长到 learning_rate, 而后再缓慢衰减，默认为0.01。 +* `max_seq_length`：可选，ERNIE-Gram 模型使用的最大序列长度，最大不能超过512, 若出现显存不足，请适当调低这一参数；默认为128。 +* `output_emb_size`：可选，向量抽取模型输出向量的维度；默认为32。 +* `dropout`：可选，DiffCSE 网络前向使用的 dropout 取值；默认 0.1。 +* `margin`：可选，用于计算损失时，保持正例相对于负例的 margin 值；默认 0.0. +* `scale`：可选，在计算 cross_entropy loss 之前对 cosine 相似度进行缩放的因子；默认为 20。 +* `train_set_file`：可选，用于指定训练集的路径。 +* `eval_set_file`：可选，用于指定验证集的路径。 +* `save_dir`：可选，保存训练模型的目录； +* `save_steps`：可选，用于指定模型训练过程中每隔多少 step 保存一次模型。 +* `eval_steps`：可选，用于指定模型训练过程中每隔多少 step，使用验证集评估一次模型。 +* `seed`：可选，随机种子，默认为1000. +* `device`: 选用什么设备进行训练，可选 cpu 或 gpu。如使用 gpu 训练则参数 gpus 指定GPU卡号。 + +程序运行时将会自动进行训练，评估。同时训练过程中会自动保存模型在指定的`save_dir`中。 +如： +```text +checkpoints/ +├── model_10000 +│   ├── model_state.pdparams +│   ├── tokenizer_config.json +│   ├── special_tokens_map.json +│   └── vocab.txt +└── ... +``` + +### 模型评估 +可以运行如下命令，进行模型评估。 + +```shell +export CUDA_VISIBLE_DEVICES=0 + +python run_diffcse.py \ + --mode "eval" \ + --extractor_name "rocketqa-zh-dureader-query-encoder" \ + --generator_name "ernie-1.0" \ + --discriminator_name "ernie-1.0" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --eval_set_file ./data/test_v1.txt \ + --ckpt_dir ./checkpoints/checkpoint_10000 \ + --batch_size "8" \ + --dropout "0.1" \ + --margin "0.0" \ + --scale "20" \ + --dup_rate "0.0" \ + --seed "0" \ + --device "gpu" +``` + + +### 基于动态图模型预测 + +测试数据示例如下，： +```text +谁有狂三这张高清的 这张高清图，谁有 +英雄联盟什么英雄最好 英雄联盟最好英雄是什么 +这是什么意思，被蹭网吗 我也是醉了，这是什么意思 +现在有什么动画片好看呢？ 现在有什么好看的动画片吗？ +请问晶达电子厂现在的工资待遇怎么样要求有哪些 三星电子厂工资待遇怎么样啊 +``` + +执行如下命令开始预测： +```shell +export CUDA_VISIBLE_DEVICES=0 + +python run_diffcse.py \ + --mode "eval" \ + --extractor_name "rocketqa-zh-dureader-query-encoder" \ + --generator_name "ernie-1.0" \ + --discriminator_name "ernie-3.0-base-zh" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --infer_set_file "./data/test.txt" \ + --ckpt_dir "./checkpoints/best_spearman" \ + --save_infer_path "./infer_result.txt" \ + --batch_size "8" \ + --dropout "0.1" \ + --margin "0.0" \ + --scale "20" \ + --dup_rate "0.0" \ + --seed "0" \ + --device "gpu" + +``` + + +## Reference +[1] [1] Chuang Y S , Dangovski R , Luo H , et al. DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings[J]. arXiv e-prints, 2022. https://arxiv.org/pdf/2204.10298.pdf. \ No newline at end of file diff --git a/examples/text_matching/diffcse/custom_ernie.py b/examples/text_matching/diffcse/custom_ernie.py new file mode 100644 index 000000000000..0c0c910feb38 --- /dev/null +++ b/examples/text_matching/diffcse/custom_ernie.py @@ -0,0 +1,1174 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import paddle +import paddle.nn as nn +import paddle.nn.functional as F + +from paddlenlp.transformers import PretrainedModel, register_base_model + +__all__ = [ + 'ErnieModel', 'ErniePretrainedModel', 'ErnieForSequenceClassification', + 'ErnieForTokenClassification', 'ErnieForQuestionAnswering', + 'ErnieForPretraining', 'ErniePretrainingCriterion', 'ErnieForMaskedLM', + 'ErnieForMultipleChoice' +] + + +class ErnieEmbeddings(nn.Layer): + r""" + Include embeddings from word, position and token_type embeddings. + """ + + def __init__(self, + vocab_size, + hidden_size=768, + hidden_dropout_prob=0.1, + max_position_embeddings=512, + type_vocab_size=2, + pad_token_id=0, + weight_attr=None, + task_type_vocab_size=3, + task_id=0, + use_task_id=False): + super(ErnieEmbeddings, self).__init__() + + self.word_embeddings = nn.Embedding(vocab_size, + hidden_size, + padding_idx=pad_token_id, + weight_attr=weight_attr) + self.position_embeddings = nn.Embedding(max_position_embeddings, + hidden_size, + weight_attr=weight_attr) + self.token_type_embeddings = nn.Embedding(type_vocab_size, + hidden_size, + weight_attr=weight_attr) + self.use_task_id = use_task_id + self.task_id = task_id + if self.use_task_id: + self.task_type_embeddings = nn.Embedding(task_type_vocab_size, + hidden_size, + weight_attr=weight_attr) + self.layer_norm = nn.LayerNorm(hidden_size) + self.dropout = nn.Dropout(hidden_dropout_prob) + + def forward(self, + input_ids, + token_type_ids=None, + position_ids=None, + task_type_ids=None): + if position_ids is None: + # maybe need use shape op to unify static graph and dynamic graph + #seq_length = input_ids.shape[1] + ones = paddle.ones_like(input_ids, dtype="int64") + seq_length = paddle.cumsum(ones, axis=1) + position_ids = seq_length - ones + position_ids.stop_gradient = True + if token_type_ids is None: + token_type_ids = paddle.zeros_like(input_ids, dtype="int64") + input_embedings = self.word_embeddings(input_ids) + position_embeddings = self.position_embeddings(position_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = input_embedings + position_embeddings + token_type_embeddings + if self.use_task_id: + if task_type_ids is None: + task_type_ids = paddle.ones_like(input_ids, + dtype="int64") * self.task_id + task_type_embeddings = self.task_type_embeddings(task_type_ids) + embeddings = embeddings + task_type_embeddings + embeddings = self.layer_norm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class ErniePooler(nn.Layer): + + def __init__(self, hidden_size, weight_attr=None): + super(ErniePooler, self).__init__() + self.dense = nn.Linear(hidden_size, + hidden_size, + weight_attr=weight_attr) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class ErniePretrainedModel(PretrainedModel): + r""" + An abstract class for pretrained ERNIE models. It provides ERNIE related + `model_config_file`, `pretrained_init_configuration`, `resource_files_names`, + `pretrained_resource_files_map`, `base_model_prefix` for downloading and + loading pretrained models. + Refer to :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details. + """ + + model_config_file = "model_config.json" + pretrained_init_configuration = { + # Deprecated, alias for ernie-1.0-base-zh + "ernie-1.0": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "relu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "max_position_embeddings": 513, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "type_vocab_size": 2, + "vocab_size": 18000, + "pad_token_id": 0, + }, + "ernie-1.0-base-zh": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "relu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "max_position_embeddings": 513, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "type_vocab_size": 2, + "vocab_size": 18000, + "pad_token_id": 0, + }, + "ernie-1.0-large-zh-cw": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "relu", + "hidden_dropout_prob": 0.1, + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, # it is 3072 instead of 4096 + "max_position_embeddings": 512, + "num_attention_heads": 16, + "num_hidden_layers": 24, + "type_vocab_size": 2, + "vocab_size": 18000, + "pad_token_id": 0, + }, + "ernie-tiny": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "relu", + "hidden_dropout_prob": 0.1, + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 4096, + "max_position_embeddings": 600, + "num_attention_heads": 16, + "num_hidden_layers": 3, + "type_vocab_size": 2, + "vocab_size": 50006, + "pad_token_id": 0, + }, + "ernie-2.0-base-zh": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "relu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "max_position_embeddings": 513, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "type_vocab_size": 4, + "vocab_size": 18000, + }, + "ernie-2.0-large-zh": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "relu", + "hidden_dropout_prob": 0.1, + "hidden_size": 1024, + "intermediate_size": 4096, # special for large model + "initializer_range": 0.02, + "max_position_embeddings": 512, + "num_attention_heads": 16, + "num_hidden_layers": 24, + "type_vocab_size": 4, + "vocab_size": 12800, + }, + "ernie-2.0-base-en": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "max_position_embeddings": 512, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "type_vocab_size": 4, + "vocab_size": 30522, + "pad_token_id": 0, + }, + "ernie-2.0-base-en-finetuned-squad": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "max_position_embeddings": 512, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "type_vocab_size": 4, + "vocab_size": 30522, + "pad_token_id": 0, + }, + "ernie-2.0-large-en": { + "attention_probs_dropout_prob": 0.1, + "intermediate_size": 4096, # special for ernie-2.0-large-en + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 1024, + "initializer_range": 0.02, + "max_position_embeddings": 512, + "num_attention_heads": 16, + "num_hidden_layers": 24, + "type_vocab_size": 4, + "vocab_size": 30522, + "pad_token_id": 0, + }, + "rocketqa-zh-dureader-query-encoder": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "relu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "max_position_embeddings": 513, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "type_vocab_size": 2, + "vocab_size": 18000, + "pad_token_id": 0, + }, + "rocketqa-zh-dureader-para-encoder": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "relu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "max_position_embeddings": 513, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "type_vocab_size": 2, + "vocab_size": 18000, + "pad_token_id": 0, + }, + "rocketqa-v1-marco-query-encoder": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "max_position_embeddings": 512, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "type_vocab_size": 4, + "vocab_size": 30522, + "pad_token_id": 0, + }, + "rocketqa-v1-marco-para-encoder": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "max_position_embeddings": 512, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "type_vocab_size": 4, + "vocab_size": 30522, + "pad_token_id": 0, + }, + "rocketqa-zh-dureader-cross-encoder": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "relu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "max_position_embeddings": 513, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "type_vocab_size": 2, + "vocab_size": 18000, + "pad_token_id": 0, + }, + "rocketqa-v1-marco-cross-encoder": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "max_position_embeddings": 512, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "type_vocab_size": 4, + "vocab_size": 30522, + "pad_token_id": 0, + }, + "ernie-3.0-xbase-zh": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "intermediate_size": 4096, # special for large model + "hidden_size": 1024, + "initializer_range": 0.02, + "max_position_embeddings": 2048, + "num_attention_heads": 16, + "num_hidden_layers": 20, + "task_type_vocab_size": 16, + "type_vocab_size": 4, + "use_task_id": True, + "vocab_size": 40000 + }, + "ernie-3.0-base-zh": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "max_position_embeddings": 2048, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "task_type_vocab_size": 3, + "type_vocab_size": 4, + "use_task_id": True, + "vocab_size": 40000 + }, + "ernie-3.0-medium-zh": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "intermediate_size": 3072, + "initializer_range": 0.02, + "max_position_embeddings": 2048, + "num_attention_heads": 12, + "num_hidden_layers": 6, + "task_type_vocab_size": 16, + "type_vocab_size": 4, + "use_task_id": True, + "vocab_size": 40000 + }, + "ernie-3.0-mini-zh": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 384, + "intermediate_size": 1536, + "initializer_range": 0.02, + "max_position_embeddings": 2048, + "num_attention_heads": 12, + "num_hidden_layers": 6, + "task_type_vocab_size": 16, + "type_vocab_size": 4, + "use_task_id": True, + "vocab_size": 40000 + }, + "ernie-3.0-micro-zh": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 384, + "intermediate_size": 1536, + "initializer_range": 0.02, + "max_position_embeddings": 2048, + "num_attention_heads": 12, + "num_hidden_layers": 4, + "task_type_vocab_size": 16, + "type_vocab_size": 4, + "use_task_id": True, + "vocab_size": 40000 + }, + "ernie-3.0-nano-zh": { + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 312, + "intermediate_size": 1248, + "initializer_range": 0.02, + "max_position_embeddings": 2048, + "num_attention_heads": 12, + "num_hidden_layers": 4, + "task_type_vocab_size": 16, + "type_vocab_size": 4, + "use_task_id": True, + "vocab_size": 40000 + }, + } + resource_files_names = {"model_state": "model_state.pdparams"} + pretrained_resource_files_map = { + "model_state": { + # Deprecated, alias for ernie-1.0-base-zh + "ernie-1.0": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie/ernie_v1_chn_base.pdparams", + "ernie-1.0-base-zh": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie/ernie_v1_chn_base.pdparams", + "ernie-1.0-large-zh-cw": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie/ernie_1.0_large_zh_cw.pdparams", + "ernie-tiny": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_tiny/ernie_tiny.pdparams", + "ernie-2.0-base-zh": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_2.0/ernie_2.0_base_zh.pdparams", + "ernie-2.0-large-zh": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_2.0/ernie_2.0_large_zh.pdparams", + "ernie-2.0-base-en": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_base/ernie_v2_eng_base.pdparams", + "ernie-2.0-base-en-finetuned-squad": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_base/ernie_v2_eng_base_finetuned_squad.pdparams", + "ernie-2.0-large-en": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_large/ernie_v2_eng_large.pdparams", + "rocketqa-zh-dureader-query-encoder": + "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa_zh_dureader_query_encoder.pdparams", + "rocketqa-zh-dureader-para-encoder": + "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa_zh_dureader_para_encoder.pdparams", + "rocketqa-v1-marco-query-encoder": + "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa_v1_marco_query_encoder.pdparams", + "rocketqa-v1-marco-para-encoder": + "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa_v1_marco_para_encoder.pdparams", + "rocketqa-zh-dureader-cross-encoder": + "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa_zh_dureader_cross_encoder.pdparams", + "rocketqa-v1-marco-cross-encoder": + "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa_v1_marco_cross_encoder.pdparams", + "ernie-3.0-base-zh": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_base_zh.pdparams", + "ernie-3.0-xbase-zh": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_xbase_zh.pdparams", + "ernie-3.0-medium-zh": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_medium_zh.pdparams", + "ernie-3.0-mini-zh": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_mini_zh.pdparams", + "ernie-3.0-micro-zh": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_micro_zh.pdparams", + "ernie-3.0-nano-zh": + "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_nano_zh.pdparams", + } + } + base_model_prefix = "ernie" + + def init_weights(self, layer): + """ Initialization hook """ + if isinstance(layer, (nn.Linear, nn.Embedding)): + # only support dygraph, use truncated_normal and make it inplace + # and configurable later + if isinstance(layer.weight, paddle.Tensor): + layer.weight.set_value( + paddle.tensor.normal(mean=0.0, + std=self.initializer_range if hasattr( + self, "initializer_range") else + self.ernie.config["initializer_range"], + shape=layer.weight.shape)) + elif isinstance(layer, nn.LayerNorm): + layer._epsilon = 1e-12 + + +@register_base_model +class ErnieModel(ErniePretrainedModel): + r""" + The bare ERNIE Model transformer outputting raw hidden-states. + This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`. + Refer to the superclass documentation for the generic methods. + This model is also a Paddle `paddle.nn.Layer `__ subclass. Use it as a regular Paddle Layer + and refer to the Paddle documentation for all matter related to general usage and behavior. + Args: + vocab_size (int): + Vocabulary size of `inputs_ids` in `ErnieModel`. Also is the vocab size of token embedding matrix. + Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `ErnieModel`. + hidden_size (int, optional): + Dimensionality of the embedding layer, encoder layers and pooler layer. Defaults to `768`. + num_hidden_layers (int, optional): + Number of hidden layers in the Transformer encoder. Defaults to `12`. + num_attention_heads (int, optional): + Number of attention heads for each attention layer in the Transformer encoder. + Defaults to `12`. + intermediate_size (int, optional): + Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors + to ff layers are firstly projected from `hidden_size` to `intermediate_size`, + and then projected back to `hidden_size`. Typically `intermediate_size` is larger than `hidden_size`. + Defaults to `3072`. + hidden_act (str, optional): + The non-linear activation function in the feed-forward layer. + ``"gelu"``, ``"relu"`` and any other paddle supported activation functions + are supported. Defaults to `"gelu"`. + hidden_dropout_prob (float, optional): + The dropout probability for all fully connected layers in the embeddings and encoder. + Defaults to `0.1`. + attention_probs_dropout_prob (float, optional): + The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target. + Defaults to `0.1`. + max_position_embeddings (int, optional): + The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input + sequence. Defaults to `512`. + type_vocab_size (int, optional): + The vocabulary size of the `token_type_ids`. + Defaults to `2`. + initializer_range (float, optional): + The standard deviation of the normal initializer for initializing all weight matrices. + Defaults to `0.02`. + + .. note:: + A normal_initializer initializes weight matrices as normal distributions. + See :meth:`ErniePretrainedModel._init_weights()` for how weights are initialized in `ErnieModel`. + pad_token_id(int, optional): + The index of padding token in the token vocabulary. + Defaults to `0`. + """ + + def __init__(self, + vocab_size, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + max_position_embeddings=512, + type_vocab_size=2, + initializer_range=0.02, + pad_token_id=0, + task_type_vocab_size=3, + task_id=0, + use_task_id=False): + super(ErnieModel, self).__init__() + self.pad_token_id = pad_token_id + self.initializer_range = initializer_range + weight_attr = paddle.ParamAttr( + initializer=nn.initializer.TruncatedNormal( + mean=0.0, std=self.initializer_range)) + self.embeddings = ErnieEmbeddings(vocab_size, hidden_size, + hidden_dropout_prob, + max_position_embeddings, + type_vocab_size, pad_token_id, + weight_attr, task_type_vocab_size, + task_id, use_task_id) + encoder_layer = nn.TransformerEncoderLayer( + hidden_size, + num_attention_heads, + intermediate_size, + dropout=hidden_dropout_prob, + activation=hidden_act, + attn_dropout=attention_probs_dropout_prob, + act_dropout=0, + weight_attr=weight_attr, + normalize_before=False) + self.encoder = nn.TransformerEncoder(encoder_layer, num_hidden_layers) + self.pooler = ErniePooler(hidden_size, weight_attr) + self.apply(self.init_weights) + + def forward(self, + input_ids, + token_type_ids=None, + position_ids=None, + attention_mask=None, + task_type_ids=None, + cls_input=None): + r""" + Args: + input_ids (Tensor): + Indices of input sequence tokens in the vocabulary. They are + numerical representations of tokens that build the input sequence. + It's data type should be `int64` and has a shape of [batch_size, sequence_length]. + token_type_ids (Tensor, optional): + Segment token indices to indicate different portions of the inputs. + Selected in the range ``[0, type_vocab_size - 1]``. + If `type_vocab_size` is 2, which means the inputs have two portions. + Indices can either be 0 or 1: + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + Its data type should be `int64` and it has a shape of [batch_size, sequence_length]. + Defaults to `None`, which means we don't add segment embeddings. + position_ids (Tensor, optional): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0, + max_position_embeddings - 1]``. + Shape as `[batch_size, num_tokens]` and dtype as int64. Defaults to `None`. + attention_mask (Tensor, optional): + Mask used in multi-head attention to avoid performing attention on to some unwanted positions, + usually the paddings or the subsequent positions. + Its data type can be int, float and bool. + When the data type is bool, the `masked` tokens have `False` values and the others have `True` values. + When the data type is int, the `masked` tokens have `0` values and the others have `1` values. + When the data type is float, the `masked` tokens have `-INF` values and the others have `0` values. + It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`. + For example, its shape can be [batch_size, sequence_length], [batch_size, sequence_length, sequence_length], + [batch_size, num_attention_heads, sequence_length, sequence_length]. + We use whole-word-mask in ERNIE, so the whole word will have the same value. For example, "使用" as a word, + "使" and "用" will have the same value. + Defaults to `None`, which means nothing needed to be prevented attention to. + Returns: + tuple: Returns tuple (``sequence_output``, ``pooled_output``). + With the fields: + - `sequence_output` (Tensor): + Sequence of hidden-states at the last layer of the model. + It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size]. + - `pooled_output` (Tensor): + The output of first token (`[CLS]`) in sequence. + We "pool" the model by simply taking the hidden state corresponding to the first token. + Its data type should be float32 and its shape is [batch_size, hidden_size]. + Example: + .. code-block:: + import paddle + from paddlenlp.transformers import ErnieModel, ErnieTokenizer + tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') + model = ErnieModel.from_pretrained('ernie-1.0') + inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") + inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} + sequence_output, pooled_output = model(**inputs) + """ + if attention_mask is None: + attention_mask = paddle.unsqueeze( + (input_ids == self.pad_token_id).astype( + self.pooler.dense.weight.dtype) * -1e4, + axis=[1, 2]) + # For 2D attention_mask from tokenizer + elif attention_mask.ndim == 2: + attention_mask = paddle.unsqueeze( + attention_mask, axis=[1, 2]).astype(paddle.get_default_dtype()) + attention_mask = (1.0 - attention_mask) * -1e4 + attention_mask.stop_gradient = True + + embedding_output = self.embeddings(input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + task_type_ids=task_type_ids) + + if cls_input is not None: + embedding_output = paddle.concat([cls_input.unsqueeze(1), embedding_output[:, 1:, :]], axis=1) + + encoder_outputs = self.encoder(embedding_output, attention_mask) + sequence_output = encoder_outputs + pooled_output = self.pooler(sequence_output) + return sequence_output, pooled_output + + +class ErnieForSequenceClassification(ErniePretrainedModel): + r""" + Ernie Model with a linear layer on top of the output layer, + designed for sequence classification/regression tasks like GLUE tasks. + Args: + ernie (ErnieModel): + An instance of `paddlenlp.transformers.ErnieModel`. + num_classes (int, optional): + The number of classes. Default to `2`. + dropout (float, optional): + The dropout probability for output of ERNIE. + If None, use the same value as `hidden_dropout_prob` + of `paddlenlp.transformers.ErnieModel` instance. Defaults to `None`. + """ + + def __init__(self, ernie, num_classes=2, dropout=None): + super(ErnieForSequenceClassification, self).__init__() + self.num_classes = num_classes + self.ernie = ernie # allow ernie to be config + self.dropout = nn.Dropout(dropout if dropout is not None else self. + ernie.config["hidden_dropout_prob"]) + self.classifier = nn.Linear(self.ernie.config["hidden_size"], + num_classes) + self.apply(self.init_weights) + + def forward(self, + input_ids, + token_type_ids=None, + position_ids=None, + attention_mask=None): + r""" + Args: + input_ids (Tensor): + See :class:`ErnieModel`. + token_type_ids (Tensor, optional): + See :class:`ErnieModel`. + position_ids (Tensor, optional): + See :class:`ErnieModel`. + attention_mask (Tensor, optional): + See :class:`ErnieModel`. + Returns: + Tensor: Returns tensor `logits`, a tensor of the input text classification logits. + Shape as `[batch_size, num_classes]` and dtype as float32. + Example: + .. code-block:: + import paddle + from paddlenlp.transformers import ErnieForSequenceClassification, ErnieTokenizer + tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') + model = ErnieForSequenceClassification.from_pretrained('ernie-1.0') + inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") + inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} + logits = model(**inputs) + """ + _, pooled_output = self.ernie(input_ids, + token_type_ids=token_type_ids, + position_ids=position_ids, + attention_mask=attention_mask) + + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + return logits + + +class ErnieForQuestionAnswering(ErniePretrainedModel): + """ + Ernie Model with a linear layer on top of the hidden-states + output to compute `span_start_logits` and `span_end_logits`, + designed for question-answering tasks like SQuAD. + Args: + ernie (`ErnieModel`): + An instance of `ErnieModel`. + """ + + def __init__(self, ernie): + super(ErnieForQuestionAnswering, self).__init__() + self.ernie = ernie # allow ernie to be config + self.classifier = nn.Linear(self.ernie.config["hidden_size"], 2) + self.apply(self.init_weights) + + def forward(self, + input_ids, + token_type_ids=None, + position_ids=None, + attention_mask=None): + r""" + Args: + input_ids (Tensor): + See :class:`ErnieModel`. + token_type_ids (Tensor, optional): + See :class:`ErnieModel`. + position_ids (Tensor, optional): + See :class:`ErnieModel`. + attention_mask (Tensor, optional): + See :class:`ErnieModel`. + Returns: + tuple: Returns tuple (`start_logits`, `end_logits`). + With the fields: + - `start_logits` (Tensor): + A tensor of the input token classification logits, indicates the start position of the labelled span. + Its data type should be float32 and its shape is [batch_size, sequence_length]. + - `end_logits` (Tensor): + A tensor of the input token classification logits, indicates the end position of the labelled span. + Its data type should be float32 and its shape is [batch_size, sequence_length]. + Example: + .. code-block:: + import paddle + from paddlenlp.transformers import ErnieForQuestionAnswering, ErnieTokenizer + tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') + model = ErnieForQuestionAnswering.from_pretrained('ernie-1.0') + inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") + inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} + logits = model(**inputs) + """ + + sequence_output, _ = self.ernie(input_ids, + token_type_ids=token_type_ids, + position_ids=position_ids, + attention_mask=attention_mask) + + logits = self.classifier(sequence_output) + logits = paddle.transpose(logits, perm=[2, 0, 1]) + start_logits, end_logits = paddle.unstack(x=logits, axis=0) + + return start_logits, end_logits + + +class ErnieForTokenClassification(ErniePretrainedModel): + r""" + ERNIE Model with a linear layer on top of the hidden-states output layer, + designed for token classification tasks like NER tasks. + Args: + ernie (`ErnieModel`): + An instance of `ErnieModel`. + num_classes (int, optional): + The number of classes. Defaults to `2`. + dropout (float, optional): + The dropout probability for output of ERNIE. + If None, use the same value as `hidden_dropout_prob` + of `ErnieModel` instance `ernie`. Defaults to `None`. + """ + + def __init__(self, ernie, num_classes=2, dropout=None): + super(ErnieForTokenClassification, self).__init__() + self.num_classes = num_classes + self.ernie = ernie # allow ernie to be config + self.dropout = nn.Dropout(dropout if dropout is not None else self. + ernie.config["hidden_dropout_prob"]) + self.classifier = nn.Linear(self.ernie.config["hidden_size"], + num_classes) + self.apply(self.init_weights) + + def forward(self, + input_ids, + token_type_ids=None, + position_ids=None, + attention_mask=None): + r""" + Args: + input_ids (Tensor): + See :class:`ErnieModel`. + token_type_ids (Tensor, optional): + See :class:`ErnieModel`. + position_ids (Tensor, optional): + See :class:`ErnieModel`. + attention_mask (Tensor, optional): + See :class:`ErnieModel`. + Returns: + Tensor: Returns tensor `logits`, a tensor of the input token classification logits. + Shape as `[batch_size, sequence_length, num_classes]` and dtype as `float32`. + Example: + .. code-block:: + import paddle + from paddlenlp.transformers import ErnieForTokenClassification, ErnieTokenizer + tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') + model = ErnieForTokenClassification.from_pretrained('ernie-1.0') + inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") + inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} + logits = model(**inputs) + """ + sequence_output, _ = self.ernie(input_ids, + token_type_ids=token_type_ids, + position_ids=position_ids, + attention_mask=attention_mask) + + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + return logits + + +class ErnieLMPredictionHead(nn.Layer): + r""" + Ernie Model with a `language modeling` head on top. + """ + + def __init__( + self, + hidden_size, + vocab_size, + activation, + embedding_weights=None, + weight_attr=None, + ): + super(ErnieLMPredictionHead, self).__init__() + + self.transform = nn.Linear(hidden_size, + hidden_size, + weight_attr=weight_attr) + self.activation = getattr(nn.functional, activation) + self.layer_norm = nn.LayerNorm(hidden_size) + self.decoder_weight = self.create_parameter( + shape=[vocab_size, hidden_size], + dtype=self.transform.weight.dtype, + attr=weight_attr, + is_bias=False) if embedding_weights is None else embedding_weights + self.decoder_bias = self.create_parameter( + shape=[vocab_size], dtype=self.decoder_weight.dtype, is_bias=True) + + def forward(self, hidden_states, masked_positions=None): + if masked_positions is not None: + hidden_states = paddle.reshape(hidden_states, + [-1, hidden_states.shape[-1]]) + hidden_states = paddle.tensor.gather(hidden_states, + masked_positions) + # gather masked tokens might be more quick + hidden_states = self.transform(hidden_states) + hidden_states = self.activation(hidden_states) + hidden_states = self.layer_norm(hidden_states) + hidden_states = paddle.tensor.matmul( + hidden_states, self.decoder_weight, + transpose_y=True) + self.decoder_bias + return hidden_states + + +class ErniePretrainingHeads(nn.Layer): + + def __init__( + self, + hidden_size, + vocab_size, + activation, + embedding_weights=None, + weight_attr=None, + ): + super(ErniePretrainingHeads, self).__init__() + self.predictions = ErnieLMPredictionHead(hidden_size, vocab_size, + activation, embedding_weights, + weight_attr) + self.seq_relationship = nn.Linear(hidden_size, + 2, + weight_attr=weight_attr) + + def forward(self, sequence_output, pooled_output, masked_positions=None): + prediction_scores = self.predictions(sequence_output, masked_positions) + seq_relationship_score = self.seq_relationship(pooled_output) + return prediction_scores, seq_relationship_score + + +class ErnieForPretraining(ErniePretrainedModel): + r""" + Ernie Model with a `masked language modeling` head and a `sentence order prediction` head + on top. + """ + + def __init__(self, ernie): + super(ErnieForPretraining, self).__init__() + self.ernie = ernie + weight_attr = paddle.ParamAttr( + initializer=nn.initializer.TruncatedNormal( + mean=0.0, std=self.ernie.initializer_range)) + self.cls = ErniePretrainingHeads( + self.ernie.config["hidden_size"], + self.ernie.config["vocab_size"], + self.ernie.config["hidden_act"], + embedding_weights=self.ernie.embeddings.word_embeddings.weight, + weight_attr=weight_attr, + ) + + self.apply(self.init_weights) + + def forward(self, + input_ids, + token_type_ids=None, + position_ids=None, + attention_mask=None, + masked_positions=None): + r""" + Args: + input_ids (Tensor): + See :class:`ErnieModel`. + token_type_ids (Tensor, optional): + See :class:`ErnieModel`. + position_ids (Tensor, optional): + See :class:`ErnieModel`. + attention_mask (Tensor, optional): + See :class:`ErnieModel`. + Returns: + tuple: Returns tuple (``prediction_scores``, ``seq_relationship_score``). + With the fields: + - `prediction_scores` (Tensor): + The scores of masked token prediction. Its data type should be float32. + If `masked_positions` is None, its shape is [batch_size, sequence_length, vocab_size]. + Otherwise, its shape is [batch_size, mask_token_num, vocab_size]. + - `seq_relationship_score` (Tensor): + The scores of next sentence prediction. + Its data type should be float32 and its shape is [batch_size, 2]. + """ + with paddle.static.amp.fp16_guard(): + outputs = self.ernie(input_ids, + token_type_ids=token_type_ids, + position_ids=position_ids, + attention_mask=attention_mask) + sequence_output, pooled_output = outputs[:2] + prediction_scores, seq_relationship_score = self.cls( + sequence_output, pooled_output, masked_positions) + return prediction_scores, seq_relationship_score + + +class ErniePretrainingCriterion(paddle.nn.Layer): + r""" + The loss output of Ernie Model during the pretraining: + a `masked language modeling` head and a `next sentence prediction (classification)` head. + """ + + def __init__(self, with_nsp_loss=True): + super(ErniePretrainingCriterion, self).__init__() + self.with_nsp_loss = with_nsp_loss + #self.loss_fn = paddle.nn.loss.CrossEntropyLoss(ignore_index=-1) + + def forward(self, + prediction_scores, + seq_relationship_score, + masked_lm_labels, + next_sentence_labels=None): + """ + Args: + prediction_scores(Tensor): + The scores of masked token prediction. Its data type should be float32. + If `masked_positions` is None, its shape is [batch_size, sequence_length, vocab_size]. + Otherwise, its shape is [batch_size, mask_token_num, vocab_size] + seq_relationship_score(Tensor): + The scores of next sentence prediction. Its data type should be float32 and + its shape is [batch_size, 2] + masked_lm_labels(Tensor): + The labels of the masked language modeling, its dimensionality is equal to `prediction_scores`. + Its data type should be int64. If `masked_positions` is None, its shape is [batch_size, sequence_length, 1]. + Otherwise, its shape is [batch_size, mask_token_num, 1] + next_sentence_labels(Tensor): + The labels of the next sentence prediction task, the dimensionality of `next_sentence_labels` + is equal to `seq_relation_labels`. Its data type should be int64 and + its shape is [batch_size, 1] + Returns: + Tensor: The pretraining loss, equals to the sum of `masked_lm_loss` plus the mean of `next_sentence_loss`. + Its data type should be float32 and its shape is [1]. + """ + + with paddle.static.amp.fp16_guard(): + masked_lm_loss = F.cross_entropy(prediction_scores, + masked_lm_labels, + ignore_index=-1, + reduction='none') + + if not self.with_nsp_loss: + return paddle.mean(masked_lm_loss) + + next_sentence_loss = F.cross_entropy(seq_relationship_score, + next_sentence_labels, + reduction='none') + return paddle.mean(masked_lm_loss), paddle.mean(next_sentence_loss) + + +class ErnieOnlyMLMHead(nn.Layer): + + def __init__(self, hidden_size, vocab_size, activation, embedding_weights): + super().__init__() + self.predictions = ErnieLMPredictionHead( + hidden_size=hidden_size, + vocab_size=vocab_size, + activation=activation, + embedding_weights=embedding_weights) + + def forward(self, sequence_output, masked_positions=None): + prediction_scores = self.predictions(sequence_output, masked_positions) + return prediction_scores + + +class ErnieForMaskedLM(ErniePretrainedModel): + """ + Ernie Model with a `masked language modeling` head on top. + Args: + ernie (:class:`ErnieModel`): + An instance of :class:`ErnieModel`. + """ + + def __init__(self, ernie): + super(ErnieForMaskedLM, self).__init__() + self.ernie = ernie + self.cls = ErnieOnlyMLMHead( + self.ernie.config["hidden_size"], + self.ernie.config["vocab_size"], + self.ernie.config["hidden_act"], + embedding_weights=self.ernie.embeddings.word_embeddings.weight) + + self.apply(self.init_weights) + + def forward(self, + input_ids, + token_type_ids=None, + position_ids=None, + attention_mask=None): + r""" + Args: + input_ids (Tensor): + See :class:`ErnieModel`. + token_type_ids (Tensor, optional): + See :class:`ErnieModel`. + position_ids (Tensor, optional): + See :class:`ErnieModel`. + attention_mask (Tensor, optional): + See :class:`ErnieModel`. + Returns: + Tensor: Returns tensor `prediction_scores`, The scores of masked token prediction. + Its data type should be float32 and shape is [batch_size, sequence_length, vocab_size]. + Example: + .. code-block:: + import paddle + from paddlenlp.transformers import ErnieForMaskedLM, ErnieTokenizer + tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') + model = ErnieForMaskedLM.from_pretrained('ernie-1.0') + + inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") + inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} + logits = model(**inputs) + print(logits.shape) + # [1, 17, 18000] + """ + + outputs = self.ernie(input_ids, + token_type_ids=token_type_ids, + position_ids=position_ids, + attention_mask=attention_mask) + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output, masked_positions=None) + return prediction_scores + + +class ErnieForMultipleChoice(ErniePretrainedModel): + """ + Ernie Model with a linear layer on top of the hidden-states output layer, + designed for multiple choice tasks like RocStories/SWAG tasks. + + Args: + ernie (:class:`ErnieModel`): + An instance of ErnieModel. + num_choices (int, optional): + The number of choices. Defaults to `2`. + dropout (float, optional): + The dropout probability for output of Ernie. + If None, use the same value as `hidden_dropout_prob` of `ErnieModel` + instance `ernie`. Defaults to None. + """ + + def __init__(self, ernie, num_choices=2, dropout=None): + super(ErnieForMultipleChoice, self).__init__() + self.num_choices = num_choices + self.ernie = ernie + self.dropout = nn.Dropout(dropout if dropout is not None else self. + ernie.config["hidden_dropout_prob"]) + self.classifier = nn.Linear(self.ernie.config["hidden_size"], 1) + self.apply(self.init_weights) + + def forward(self, + input_ids, + token_type_ids=None, + position_ids=None, + attention_mask=None): + r""" + The ErnieForMultipleChoice forward method, overrides the __call__() special method. + Args: + input_ids (Tensor): + See :class:`ErnieModel` and shape as [batch_size, num_choice, sequence_length]. + token_type_ids(Tensor, optional): + See :class:`ErnieModel` and shape as [batch_size, num_choice, sequence_length]. + position_ids(Tensor, optional): + See :class:`ErnieModel` and shape as [batch_size, num_choice, sequence_length]. + attention_mask (list, optional): + See :class:`ErnieModel` and shape as [batch_size, num_choice, sequence_length]. + Returns: + Tensor: Returns tensor `reshaped_logits`, a tensor of the multiple choice classification logits. + Shape as `[batch_size, num_choice]` and dtype as `float32`. + """ + # input_ids: [bs, num_choice, seq_l] + input_ids = input_ids.reshape(shape=( + -1, input_ids.shape[-1])) # flat_input_ids: [bs*num_choice,seq_l] + + if position_ids is not None: + position_ids = position_ids.reshape(shape=(-1, + position_ids.shape[-1])) + if token_type_ids is not None: + token_type_ids = token_type_ids.reshape( + shape=(-1, token_type_ids.shape[-1])) + + if attention_mask is not None: + attention_mask = attention_mask.reshape( + shape=(-1, attention_mask.shape[-1])) + + _, pooled_output = self.ernie(input_ids, + token_type_ids=token_type_ids, + position_ids=position_ids, + attention_mask=attention_mask) + pooled_output = self.dropout(pooled_output) + + logits = self.classifier(pooled_output) # logits: (bs*num_choice,1) + reshaped_logits = logits.reshape( + shape=(-1, self.num_choices)) # logits: (bs, num_choice) + + return reshaped_logits diff --git a/examples/text_matching/diffcse/data.py b/examples/text_matching/diffcse/data.py new file mode 100644 index 000000000000..3dae95d40606 --- /dev/null +++ b/examples/text_matching/diffcse/data.py @@ -0,0 +1,166 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import paddle + +import os +import random +import numpy as np + + +def get_special_tokens(): + return ["[PAD]", "[CLS]", "[SEP]", "[MASK]", "[UNK]"] + +def get_special_token_ids(tokenizer): + special_tokens = ["[PAD]", "[CLS]", "[SEP]", "[MASK]", "[UNK]"] + return tokenizer.convert_tokens_to_ids(special_tokens) + +def get_special_token_dict(tokenizer): + special_tokens = ["[PAD]", "[CLS]", "[SEP]", "[MASK]", "[UNK]"] + special_token_dict = dict(zip(special_tokens, tokenizer.convert_tokens_to_ids(special_tokens))) + return special_token_dict + + +def create_dataloader(dataset, mode="train", batch_size=1, batchify_fn=None, trans_fn=None): + if trans_fn: + dataset = dataset.map(trans_fn) + shuffle = True if mode == "train" else False + if mode == "train": + batch_sampler = paddle.io.DistributedBatchSampler(dataset, batch_size=batch_size, shuffle=shuffle) + else: + batch_sampler = paddle.io.BatchSampler(dataset, batch_size=batch_size, shuffle=shuffle) + return paddle.io.DataLoader(dataset=dataset, batch_sampler=batch_sampler, collate_fn=batchify_fn, return_list=True) + + +def convert_example(example, tokenizer, max_seq_length=512, do_evalute=False): + result = [] + for key, text in example.items(): + if "label" in key: + # do_evaluate + result += [example["label"]] + else: + # do_train + encoded_inputs = tokenizer(text=text, max_seq_len=max_seq_length, return_attention_mask=True) + # print(encoded_inputs) + input_ids = encoded_inputs["input_ids"] + token_type_ids = encoded_inputs["token_type_ids"] + attention_mask = encoded_inputs["attention_mask"] + result += [input_ids, token_type_ids, attention_mask] + return result + + +def read_text_single(data_path): + with open(data_path, "r", encoding="utf-8") as f: + for line in f: + data = line.rstrip() + yield {"text_a": data, "text_b": data} + +def masked_fill(x, mask, value): + y = paddle.full(x.shape, value, x.dtype) + return paddle.where(mask, y, x) + +def mask_tokens(batch_inputs, tokenizer, mlm_probability=0.15): + """ + Description: Mask input_ids for masked language modeling: 80% MASK, 10% random, 10% original + """ + mlm_inputs = batch_inputs.clone() + mlm_labels = batch_inputs.clone() + + # print(mlm_inputs) + probability_matrix = paddle.full(mlm_inputs.shape, mlm_probability) + # print(probability_matrix) + + special_tokens_mask = paddle.cast(paddle.zeros(mlm_inputs.shape), dtype=bool) + for special_token_id in get_special_token_ids(tokenizer): + special_tokens_mask |= (mlm_inputs==special_token_id) + + + probability_matrix = masked_fill(probability_matrix, special_tokens_mask, 0.0) + # print(probability_matrix) + + masked_indices = paddle.cast(paddle.bernoulli(probability_matrix), dtype=bool) + mlm_labels = masked_fill(mlm_labels, ~masked_indices, -100) + + # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) + indices_replaced = paddle.cast(paddle.bernoulli(paddle.full(mlm_inputs.shape, 0.8)), dtype=bool) & masked_indices + mlm_inputs = masked_fill(mlm_inputs, indices_replaced, tokenizer.mask_token_id) + + # 10% of the time, we replace masked input tokens with random word + indices_random = paddle.cast(paddle.bernoulli(paddle.full(mlm_inputs.shape, 0.5)) ,dtype=bool) & masked_indices & ~indices_replaced + random_words = paddle.randint(0, len(tokenizer), mlm_inputs.shape, dtype=mlm_inputs.dtype) + mlm_inputs = paddle.where(indices_random, random_words, mlm_inputs) + + # The rest of the time (10% of the time) we keep the masked input tokens unchanged + # print(mlm_inputs) + # print(mlm_labels) + return mlm_inputs, mlm_labels + + +def read_text_pair(data_path, is_infer=False): + with open(data_path, "r", encoding="utf-8") as f: + for line in f: + data = line.rstrip().split("\t") + if is_infer: + if len(data[0]) == 0 or len(data[1]) == 0: + continue + yield {"text_a": data[0], "text_b": data[1]} + else: + if len(data[0]) == 0 or len(data[1]) == 0 or len(data[2]) == 0: + continue + yield {"text_a": data[0], "text_b": data[1], "label": data[2]} + + +def word_repetition(input_ids, token_type_ids, dup_rate=0.32): + """Word Reptition Strategy""" + input_ids = input_ids.numpy().tolist() + token_type_ids = token_type_ids.numpy().tolist() + + batch_size, seq_len = len(input_ids), len(input_ids[0]) + repetitied_input_ids = [] + repetitied_token_type_ids = [] + rep_seq_len = seq_len + for batch_id in range(batch_size): + cur_input_id = input_ids[batch_id] + actual_len = np.count_nonzero(cur_input_id) + dup_word_index = [] + # If sequence length is less than 5, skip it + if (actual_len > 5): + dup_len = random.randint(a=0, b=max(2, int(dup_rate * actual_len))) + # Skip cls and sep position + dup_word_index = random.sample(list(range(1, actual_len - 1)), k=dup_len) + + r_input_id = [] + r_token_type_id = [] + for idx, word_id in enumerate(cur_input_id): + # Insert duplicate word + if idx in dup_word_index: + r_input_id.append(word_id) + r_token_type_id.append(token_type_ids[batch_id][idx]) + r_input_id.append(word_id) + r_token_type_id.append(token_type_ids[batch_id][idx]) + after_dup_len = len(r_input_id) + repetitied_input_ids.append(r_input_id) + repetitied_token_type_ids.append(r_token_type_id) + rep_seq_len = max(rep_seq_len, after_dup_len) + + # Padding the data to the same length + for batch_id in range(batch_size): + after_dup_len = len(repetitied_input_ids[batch_id]) + pad_len = rep_seq_len - after_dup_len + repetitied_input_ids[batch_id] += [0] * pad_len + repetitied_token_type_ids[batch_id] += [0] * pad_len + + return paddle.to_tensor(repetitied_input_ids), paddle.to_tensor(repetitied_token_type_ids) + diff --git a/examples/text_matching/diffcse/eval_metrics.py b/examples/text_matching/diffcse/eval_metrics.py new file mode 100644 index 000000000000..cf0b9287e271 --- /dev/null +++ b/examples/text_matching/diffcse/eval_metrics.py @@ -0,0 +1,146 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import numpy as np +from scipy import stats +from sklearn import metrics + + +def eval_metrics(labels, sims): + + eval_res = {} + + spearman_corr = stats.spearmanr(labels, sims).correlation + eval_res["spearman_corr"] = spearman_corr + print("Spearman corr --> {:.5f}\n".format(spearman_corr)) + + labels_2_cls = np.where(np.greater_equal(labels, 3), np.ones_like(labels), np.zeros_like(labels)) + + pre, rec, thr = metrics.precision_recall_curve(labels_2_cls, sims, pos_label=1) + pre_rec = np.sum([pre, rec], axis=0) + best_pre_rec = np.max(pre_rec) + best_pre = pre[np.argmax(pre_rec)] + best_rec = rec[np.argmax(pre_rec)] + best_thr = thr[np.argmax(pre_rec)] + eval_res["best_pre_rec_thr"] = [best_pre_rec, best_pre, best_rec, best_thr] + print("2-CLS best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}".format(best_pre_rec, best_pre, best_rec, best_thr)) + + pre_rec_thr = list(zip(pre, rec, thr)) + pre_rec_thr.sort(key=lambda x: x[1], reverse=True) + pre_rec_thr_at_K = None + at_K = 0.95 + for x in pre_rec_thr: + if x[0] >= at_K: + pre_rec_thr_at_K = x + break + eval_res["best_pre_rec_thr_at_K"] = pre_rec_thr_at_K + print("2-CLS best pre@{:.2f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}".format(at_K, pre_rec_thr_at_K[0], pre_rec_thr_at_K[1], pre_rec_thr_at_K[2])) + + preds_2_cls = np.where(np.greater_equal(sims, best_thr), np.ones_like(sims), np.zeros_like(sims)) + + acc_2_cls = metrics.accuracy_score(labels_2_cls, preds_2_cls) + auc_2_cls = metrics.roc_auc_score(labels_2_cls, sims) + report_2_cls = metrics.classification_report( + labels_2_cls, + preds_2_cls, + labels=[0, 1], + target_names=["0", "1"], + digits=5, + output_dict=True + ) + eval_res["acc_2_cls"] = acc_2_cls + eval_res["auc_2_cls"] = auc_2_cls + eval_res["report_2_cls"] = report_2_cls + print("2-CLS metrics are evaluated on the best thr --> {}".format(best_thr)) + print("2-CLS accuracy --> {}".format(acc_2_cls)) + print("2-CLS roc-auc --> {}".format(auc_2_cls)) + print("\t".join(["label", "precision", "recall", "f1-score", "support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("2-CLS 0", report_2_cls["0"]["precision"], report_2_cls["0"]["recall"], report_2_cls["0"]["f1-score"], report_2_cls["0"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}\n".format("2-CLS 1", report_2_cls["1"]["precision"], report_2_cls["1"]["recall"], report_2_cls["1"]["f1-score"], report_2_cls["1"]["support"])) + + best_thr_6_cls = [] + for pivot in [5, 4, 3, 2, 1]: + labels_pivot = np.where(np.greater_equal(labels, pivot), np.ones_like(labels), np.zeros_like(labels)) + pre_pivot, rec_pivot, thr_pivot = metrics.precision_recall_curve(labels_pivot, sims, pos_label=1) + pre_rec_pivot = np.sum([pre_pivot, rec_pivot], axis=0) + best_pre_rec_pivot = np.max(pre_rec_pivot) + best_pre_pivot = pre_pivot[np.argmax(pre_rec_pivot)] + best_rec_pivot = rec_pivot[np.argmax(pre_rec_pivot)] + best_thr_pivot = thr_pivot[np.argmax(pre_rec_pivot)] + best_thr_6_cls.append(best_thr_pivot) + eval_res["best_pre_rec_thr_6_cls_at_{}_pivot".format(pivot)] = [best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, best_thr_pivot] + print("6-CLS [split at {} pivot] best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}".format(pivot, best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, best_thr_pivot)) + + preds = [] + for s in sims: + s = max(s, 0) + for pivot, thr in list(zip([5, 4, 3, 2, 1, 0], best_thr_6_cls + [0.0])): + if s + 1e-8 >= thr: + preds.append(pivot) + break + + acc_6_cls = metrics.accuracy_score(labels, preds) + + report_6_cls = metrics.classification_report( + labels, + preds, + labels=[0, 1, 2, 3, 4, 5], + target_names=["0", "1", "2", "3", "4", "5"], + digits=5, + output_dict=True + ) + eval_res["acc_6_cls"] = acc_6_cls + eval_res["report_6_cls"] = report_6_cls + print("6-CLS metrics are evaluated on the best thr --> {}".format(best_thr_6_cls)) + print("6-CLS accuracy --> {}".format(acc_6_cls)) + print("\t".join(["label", "precision", "recall", "f1-score", "support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 0", report_6_cls["0"]["precision"], report_6_cls["0"]["recall"], report_6_cls["0"]["f1-score"], report_6_cls["0"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 1", report_6_cls["1"]["precision"], report_6_cls["1"]["recall"], report_6_cls["1"]["f1-score"], report_6_cls["1"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 2", report_6_cls["2"]["precision"], report_6_cls["2"]["recall"], report_6_cls["2"]["f1-score"], report_6_cls["2"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 3", report_6_cls["3"]["precision"], report_6_cls["3"]["recall"], report_6_cls["3"]["f1-score"], report_6_cls["3"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 4", report_6_cls["4"]["precision"], report_6_cls["4"]["recall"], report_6_cls["4"]["f1-score"], report_6_cls["4"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 5", report_6_cls["5"]["precision"], report_6_cls["5"]["recall"], report_6_cls["5"]["f1-score"], report_6_cls["5"]["support"])) + + return eval_res + + +def read_infer_result(infer_result): + sims = [] + with open(infer_result, 'r') as f: + for line in f: + line = line.strip() + if " --> " in line: + sim = float(line.split(' --> ')[1]) + sims.append(sim) + return sims + + +def read_infer_label(infer_label): + labels = [] + with open(infer_label, 'r') as f: + for line in f: + line = line.strip() + label = int(line.split('\t')[2]) + labels.append(label) + return labels + + +if __name__ == "__main__": + """ + example + """ + labels = read_infer_label("./data/test_v1.txt") + sims = read_infer_result("infer_res.txt") + eval_metrics(labels, sims) diff --git a/examples/text_matching/diffcse/model.py b/examples/text_matching/diffcse/model.py new file mode 100644 index 000000000000..d580dc57729e --- /dev/null +++ b/examples/text_matching/diffcse/model.py @@ -0,0 +1,245 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import numpy as np + +import paddle +import paddle.nn as nn +import paddle.nn.functional as F +from paddlenlp.transformers import AutoTokenizer, AutoModel, ErnieForMaskedLM + +from data import mask_tokens +from custom_ernie import ErnieModel as CustomErnie + + +class Extractor(nn.Layer): + def __init__(self, pretrained_model_name, dropout=None, margin=0.0, scale=20, output_emb_size=None): + super().__init__() + self.ptm = AutoModel.from_pretrained(pretrained_model_name) + self.dropout = nn.Dropout(dropout if dropout is not None else 0.1) + # if output_emb_size is greater than 0, then add Linear layer to reduce embedding_size + self.output_emb_size = output_emb_size + if output_emb_size > 0: + weight_attr = paddle.ParamAttr(initializer=paddle.nn.initializer.TruncatedNormal(std=0.02)) + self.emb_reduce_linear = paddle.nn.Linear(768, output_emb_size, weight_attr=weight_attr) + + self.margin = margin + # Used scaling cosine similarity to ease converge + self.scale = scale + + def get_pooled_embedding(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, with_pooler=False): + # Note: cls_embedding is poolerd embedding with act tanh + sequence_output, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids, attention_mask) + if not with_pooler: + ori_cls_embedding = sequence_output[:, 0, :] + if self.output_emb_size > 0: + cls_embedding = self.emb_reduce_linear(ori_cls_embedding) + + cls_embedding = self.dropout(cls_embedding) + cls_embedding = F.normalize(cls_embedding, p=2, axis=-1) + return cls_embedding, ori_cls_embedding + + def cosine_sim( + self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + query_position_ids=None, + query_attention_mask=None, + key_token_type_ids=None, + key_position_ids=None, + key_attention_mask=None, + with_pooler=False + ): + query_cls_embedding, _ = self.get_pooled_embedding( + query_input_ids, + query_token_type_ids, + query_position_ids, + query_attention_mask, + with_pooler=with_pooler + ) + key_cls_embedding, _ = self.get_pooled_embedding( + key_input_ids, + key_token_type_ids, + key_position_ids, + key_attention_mask, + with_pooler=with_pooler + ) + + cosine_sim = paddle.sum(query_cls_embedding * key_cls_embedding, axis=-1) + return cosine_sim + + def forward( + self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + query_position_ids=None, + query_attention_mask=None, + key_token_type_ids=None, + key_position_ids=None, + key_attention_mask=None, + with_pooler=False + ): + query_cls_embedding, ori_query_cls_embedding = self.get_pooled_embedding( + query_input_ids, + query_token_type_ids, + query_position_ids, + query_attention_mask, + with_pooler=with_pooler + ) + key_cls_embedding, ori_key_cls_embedding = self.get_pooled_embedding( + key_input_ids, + key_token_type_ids, + key_position_ids, + key_attention_mask, + with_pooler=with_pooler + ) + cosine_sim = paddle.matmul(query_cls_embedding, key_cls_embedding, transpose_y=True) + + # substract margin from all positive samples cosine_sim() + margin_diag = paddle.full( + shape=[query_cls_embedding.shape[0]], + fill_value=self.margin, + dtype=paddle.get_default_dtype() + ) + + cosine_sim = cosine_sim - paddle.diag(margin_diag) + + # scale cosine to ease training converge + cosine_sim *= self.scale + + labels = paddle.arange(0, query_cls_embedding.shape[0], dtype="int64") + labels = paddle.reshape(labels, shape=[-1, 1]) + + loss = F.cross_entropy(input=cosine_sim, label=labels) + ori_cls_embedding = paddle.concat([ori_query_cls_embedding, ori_key_cls_embedding], axis=0) + return loss, ori_cls_embedding + +class Discriminator(nn.Layer): + def __init__(self, ptm_model_name): + super(Discriminator, self).__init__() + self.ptm = CustomErnie.from_pretrained(ptm_model_name) + self.classifier = nn.Linear(self.ptm.config["hidden_size"], 2) + + def forward(self, input_ids, labels, cls_input, token_type_ids=None, attention_mask=None): + sequence_output, _ = self.ptm(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, cls_input=cls_input) + pred_scores = self.classifier(sequence_output) + loss = F.cross_entropy(input=pred_scores, label=labels) + + return loss + + +class DiffCSE(nn.Layer): + def __init__(self, args, tokenizer): + super(DiffCSE, self).__init__() + self.args = args + self.mode = args.mode + self.tokenizer = tokenizer + self.generator_name = args.generator_name + self.discriminator_name = args.discriminator_name + + self.extractor = Extractor(args.extractor_name, margin=args.margin, scale=args.scale, output_emb_size=args.output_emb_size) + self.generator = ErnieForMaskedLM.from_pretrained(args.generator_name) + self.discriminator = Discriminator(args.discriminator_name) + + def train_forward(self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + key_token_type_ids=None, + query_attention_mask=None, + key_attention_mask=None, + cls_token = 1 + ): + + # extract senmantic vector with extractor and then comput CL loss + loss, ori_cls_embedding = self.extractor(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask) + + + with paddle.no_grad(): + # mask tokens for query and key input_ids and then predict mask token with generator + input_ids = paddle.concat([query_input_ids, key_input_ids], axis=0) + attention_mask = paddle.concat([query_attention_mask, key_attention_mask], axis=0) + mlm_input_ids, _ = mask_tokens(paddle.concat([query_input_ids, key_input_ids], axis=0), self.tokenizer, mlm_probability=self.args.mlm_probability) + pred_tokens = self.generator(mlm_input_ids, attention_mask=attention_mask).argmax(-1) + + pred_tokens[:, 0] = cls_token + e_inputs = pred_tokens * attention_mask + replaced = pred_tokens != input_ids + e_labels = paddle.cast(replaced, dtype="int64") * attention_mask + + rtd_loss = self.discriminator(e_inputs, e_labels, cls_input=ori_cls_embedding) + loss = loss + self.args.lambda_weight * rtd_loss + + return loss + + + def test_forward(self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + key_token_type_ids=None, + query_attention_mask=None, + key_attention_mask=None + ): + + # compute cosine similarity for query and key text + cos_sim = self.extractor.cosine_sim(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask) + + return cos_sim + + def forward(self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + key_token_type_ids=None, + query_attention_mask=None, + key_attention_mask=None, + cls_token = 1, + mode="train" + ): + if mode == "train": + return self.train_forward(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask, cls_token=cls_token) + else: + return self.test_forward(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask) + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/examples/text_matching/diffcse/run_diffcse.py b/examples/text_matching/diffcse/run_diffcse.py new file mode 100644 index 000000000000..c2346b3031ac --- /dev/null +++ b/examples/text_matching/diffcse/run_diffcse.py @@ -0,0 +1,376 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import os +import time +import random +import argparse +import numpy as np +from scipy import stats +from functools import partial + +import paddle +import paddle.nn.functional as F +import paddlenlp as ppnlp +from paddlenlp.data import Stack, Tuple, Pad +from paddlenlp.datasets import load_dataset +from paddlenlp.transformers import LinearDecayWithWarmup + +from model import DiffCSE, Extractor +from utils import set_seed +from eval_metrics import eval_metrics +from data import read_text_single, read_text_pair, convert_example, create_dataloader, word_repetition + + +# yapf: disable +parser = argparse.ArgumentParser() +parser.add_argument("--mode", choices=["train", "eval", "infer"], default="infer", help="Select which mode to run model, defaults to infer.") +parser.add_argument("--extractor_name", type=str, help="The extractor model name or path that you wanna train based on.") +parser.add_argument("--generator_name", type=str, help="The generator model name or path that you wanna train based on.") +parser.add_argument("--discriminator_name", type=str, help="The discriminator model name or path that you wanna train based on.") +parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization.") +parser.add_argument("--output_emb_size", default=0, type=int, help="Output_embedding_size, 0 means use hidden_size as output embedding size.") +parser.add_argument("--train_set_file", type=str, help="The full path of train_set_file.") +parser.add_argument("--eval_set_file", type=str, help="The full path of eval_set_file.") +parser.add_argument("--infer_set_file", type=str, help="The full path of infer_set_file.") +parser.add_argument("--ckpt_dir", default=None, type=str, help="The ckpt directory where the model checkpoints will be loaded.") +parser.add_argument("--save_dir", default="./checkpoints", type=str, help="The save directory where the model checkpoints will be written.") +parser.add_argument("--save_infer_path", default="./infer_result.txt", type=str, help="The save directory where the inference result will be written.") +parser.add_argument("--save_steps", type=int, default=10000, help="Step interval for saving checkpoint.") +parser.add_argument("--eval_steps", type=int, default=10000, help="Step interval for evaluation.") +parser.add_argument("--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override ecpochs.") +parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.") +parser.add_argument("--epochs", default=1, type=int, help="Total number of training epochs to perform.") +parser.add_argument("--learning_rate", default=1e-5, type=float, help="The initial learning rate for Adam.") +parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") +parser.add_argument("--warmup_proportion", default=0.0, type=float, help="Linear warmup proption over the training process.") +parser.add_argument("--dropout", default=0.1, type=float, help="Dropout for pretrained model encoder.") +parser.add_argument("--margin", default=0.0, type=float, help="Margin beteween pos_sample and neg_samples.") +parser.add_argument("--scale", default=20, type=int, help="Scale for pair-wise margin_rank_loss.") +parser.add_argument("--dup_rate", default=0.32, type=float, help="duplicate rate for word reptition.") +parser.add_argument("--mlm_probability", default=0.15, type=float, help="The ratio for masked language model.") +parser.add_argument("--lambda_weight", default=0.15, type=float, help="The weight for RTD loss.") +parser.add_argument("--seed", type=int, default=1000, help="Random seed for initialization.") +parser.add_argument("--device", choices=["cpu", "gpu"], default="gpu", help="Select which device to train model, defaults to gpu.") +args = parser.parse_args() +# yapf: enable + + +def do_infer(model, tokenizer, data_loader): + assert isinstance(model, Extractor), "please make sure that model is instance of Extractor." + sims = [] + model.eval() + with paddle.no_grad(): + for batch in data_loader: + query_input_ids, query_token_type_ids, query_attention_mask, key_input_ids, key_token_type_ids, key_attention_mask = batch + cosine_sim = model.cosine_sim( + query_input_ids=query_input_ids, + key_input_ids=key_input_ids, + query_token_type_ids=query_token_type_ids, + key_token_type_ids=key_token_type_ids, + query_attention_mask=query_attention_mask, + key_attention_mask=key_attention_mask, + ) + sims.append(cosine_sim.numpy()) + sims = np.concatenate(sims, axis=0) + model.train() + return sims + + +def do_eval(model, tokenizer, data_loader): + assert isinstance(model, Extractor), "please make sure that model is instance of Extractor." + total_num = 0 + spearman_corr = 0.0 + sims = [] + labels = [] + model.eval() + with paddle.no_grad(): + for batch in data_loader: + query_input_ids, query_token_type_ids, query_attention_mask, key_input_ids, key_token_type_ids, key_attention_mask, label = batch + total_num += len(label) + cosine_sim = model.cosine_sim( + query_input_ids=query_input_ids, + key_input_ids=key_input_ids, + query_token_type_ids=query_token_type_ids, + key_token_type_ids=key_token_type_ids, + query_attention_mask=query_attention_mask, + key_attention_mask=key_attention_mask, + ) + sims.append(cosine_sim.numpy()) + labels.append(label.numpy()) + + sims = np.concatenate(sims, axis=0) + labels = np.concatenate(labels, axis=0) + eval_res = eval_metrics(labels, sims) + model.train() + return eval_res, total_num + + + +def do_train(model, tokenizer, train_data_loader, dev_data_loader): + num_training_steps = args.max_steps if args.max_steps > 0 else len(train_data_loader) * args.epochs + + lr_scheduler = LinearDecayWithWarmup( + args.learning_rate, + num_training_steps, + args.warmup_proportion + ) + + decay_params = [p.name for n, p in model.named_parameters() if not any(nd in n for nd in ["bias", "norm"])] + optimizer = paddle.optimizer.AdamW( + learning_rate=lr_scheduler, + parameters=model.parameters(), + weight_decay=args.weight_decay, + apply_decay_param_fun=lambda x: x in decay_params + ) + + global_step = 0 + best_spearman = 0.0 + best_pre_rec = 0.0 + best_rec95 = 0.0 + tic_train = time.time() + model = paddle.DataParallel(model) + model.train() + for epoch in range(1, args.epochs + 1): + for step, batch in enumerate(train_data_loader, start=1): + query_input_ids, query_token_type_ids, query_attention_mask, key_input_ids, key_token_type_ids, key_attention_mask = batch + + if(args.dup_rate > 0): + query_input_ids, query_token_type_ids = word_repetition(query_input_ids, query_token_type_ids, args.dup_rate) + key_input_ids, key_token_type_ids = word_repetition(key_input_ids, key_token_type_ids, args.dup_rate) + + loss = model( + query_input_ids, + key_input_ids, + query_token_type_ids=query_token_type_ids, + key_token_type_ids=key_token_type_ids, + query_attention_mask=query_attention_mask, + key_attention_mask=key_attention_mask, + cls_token=tokenizer.cls_token_id + ) + + global_step += 1 + if global_step % (args.eval_steps // 10) == 0 and rank == 0: + print("global step {}, epoch: {}, batch: {}, loss: {:.5f}, speed: {:.2f} step/s".format(global_step, epoch, step, loss.item(), (args.eval_steps // 10) / (time.time() - tic_train))) + tic_train = time.time() + + if global_step % args.eval_steps == 0 and rank == 0: + eval_res, total_num = do_eval(model._layers.extractor, tokenizer, dev_data_loader) + spearman = eval_res["spearman_corr"] + if best_spearman < spearman: + print("best checkpoint has been updated: from last best_spearman {} --> new spearman {}.".format(best_spearman, spearman)) + best_spearman = spearman + # save best model + save_dir = os.path.join(args.save_dir, "best_spearman") + if not os.path.exists(save_dir): + os.makedirs(save_dir) + save_param_path = os.path.join(save_dir, "model_state.pdparams") + paddle.save(model._layers.extractor.state_dict(), save_param_path) + tokenizer.save_pretrained(save_dir) + with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: + f.write(str(global_step)+" "+str(epoch)+ " "+ str(batch)) + + pre_rec = eval_res["best_pre_rec_thr"][0] + if best_pre_rec < pre_rec: + print("best checkpoint has been updated: from last best_pre_rec {} --> new pre_rec {}.".format(best_pre_rec, pre_rec)) + best_pre_rec = pre_rec + # save best model + save_dir = os.path.join(args.save_dir, "best_pre_rec") + if not os.path.exists(save_dir): + os.makedirs(save_dir) + save_param_path = os.path.join(save_dir, "model_state.pdparams") + paddle.save(model._layers.extractor.state_dict(), save_param_path) + tokenizer.save_pretrained(save_dir) + with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: + f.write(str(global_step)+" "+str(epoch)+ " "+ str(batch)) + + rec95 = eval_res["best_pre_rec_thr_at_K"][1] + if best_rec95 < rec95: + print("best checkpoint has been updated: from last best_rec95 {} --> new rec95 {}.".format(best_rec95, rec95)) + best_rec95 = rec95 + # save best model + save_dir = os.path.join(args.save_dir, "best_rec95") + if not os.path.exists(save_dir): + os.makedirs(save_dir) + save_param_path = os.path.join(save_dir, "model_state.pdparams") + paddle.save(model._layers.extractor.state_dict(), save_param_path) + tokenizer.save_pretrained(save_dir) + with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: + f.write(str(global_step)+" "+str(epoch)+ " "+ str(batch)) + model.train() + + loss.backward() + optimizer.step() + lr_scheduler.step() + optimizer.clear_grad() + if global_step % args.save_steps == 0 and rank == 0: + save_dir = os.path.join(args.save_dir, "checkpoint_{}".format(global_step)) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + save_param_path = os.path.join(save_dir, "model_state.pdparams") + paddle.save(model.state_dict(), save_param_path) + tokenizer.save_pretrained(save_dir) + + if args.max_steps > 0 and global_step >= args.max_steps: + return model + + +if __name__ == "__main__": + # set running environment + paddle.set_device(args.device) + rank = paddle.distributed.get_rank() + if paddle.distributed.get_world_size() > 1: + paddle.distributed.init_parallel_env() + + set_seed(args.seed) + + if not os.path.exists(args.save_dir): + os.makedirs(args.save_dir) + + # define token for processing data + tokenizer = ppnlp.transformers.AutoTokenizer.from_pretrained(args.extractor_name) + + trans_func = partial( + convert_example, + tokenizer=tokenizer, + max_seq_length=args.max_seq_length + ) + + if args.mode == "train": + start_time = time.time() + # intializing DiffCSE model + model = DiffCSE(args, tokenizer) + train_ds = load_dataset(read_text_single, data_path=args.train_set_file, lazy=False) + dev_ds = load_dataset(read_text_pair, data_path=args.eval_set_file, lazy=False) + + batchify_fn = lambda samples, fn=Tuple( + Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input + Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # query_segment + Pad(axis=0, pad_val=0), # attention_mask + Pad(axis=0, pad_val=tokenizer.pad_token_id), # key_input + Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # tilte_segment + Pad(axis=0, pad_val=0), # attention_mask + ): [data for data in fn(samples)] + dev_batchify_fn = lambda samples, fn=Tuple( + Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input + Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # query_segment + Pad(axis=0, pad_val=0), # attention_mask + Pad(axis=0, pad_val=tokenizer.pad_token_id), # key_input + Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # tilte_segment + Pad(axis=0, pad_val=0), # attention_mask + Stack(dtype="int64"), # labels + ): [data for data in fn(samples)] + + train_data_loader = create_dataloader( + train_ds, + mode="train", + batch_size=args.batch_size, + batchify_fn=batchify_fn, + trans_fn=trans_func + ) + dev_data_loader = create_dataloader( + dev_ds, + mode="eval", + batch_size=args.batch_size, + batchify_fn=dev_batchify_fn, + trans_fn=trans_func + ) + + do_train(model, tokenizer, train_data_loader, dev_data_loader) + + end_time = time.time() + print("running time {} s".format(end_time - start_time)) + + + if args.mode == "eval": + start_time = time.time() + # initalizing Extractor model for eval + model = Extractor(args.extractor_name, margin=args.margin, scale=args.scale, output_emb_size=args.output_emb_size) + # load model from saved checkpoint + if args.ckpt_dir: + init_from_ckpt = os.path.join(args.ckpt_dir, "model_state.pdparams") + if os.path.isfile(init_from_ckpt): + print("*************************initializing model from {}*****************************".format(init_from_ckpt)) + state_dict = paddle.load(init_from_ckpt) + model.set_dict(state_dict) + + dev_ds = load_dataset(read_text_pair, data_path=args.eval_set_file, lazy=False) + + dev_batchify_fn = lambda samples, fn=Tuple( + Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input + Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # query_segment + Pad(axis=0, pad_val=0), # attention_mask + Pad(axis=0, pad_val=tokenizer.pad_token_id), # key_input + Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # tilte_segment + Pad(axis=0, pad_val=0), # attention_mask + Stack(dtype="int64"), # labels + ): [data for data in fn(samples)] + + dev_data_loader = create_dataloader( + dev_ds, + mode="eval", + batch_size=args.batch_size, + batchify_fn=dev_batchify_fn, + trans_fn=trans_func + ) + + eval_res, total_num = do_eval(model, tokenizer, dev_data_loader) + + end_time = time.time() + print("running time {} s".format(end_time - start_time)) + + if args.mode == "infer": + start_time = time.time() + # initalizing Extractor model for eval + model = Extractor(args.extractor_name, margin=args.margin, scale=args.scale, output_emb_size=args.output_emb_size) + # load model from saved checkpoint + if args.ckpt_dir: + init_from_ckpt = os.path.join(args.ckpt_dir, "model_state.pdparams") + if os.path.isfile(init_from_ckpt): + print("*************************initializing model from {}*****************************".format(init_from_ckpt)) + state_dict = paddle.load(init_from_ckpt) + model.set_dict(state_dict) + + infer_ds = load_dataset(read_text_pair, data_path=args.infer_set_file, lazy=False, is_infer=True) + + batchify_fn = lambda samples, fn=Tuple( + Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input + Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # query_segment + Pad(axis=0, pad_val=0), # attention_mask + Pad(axis=0, pad_val=tokenizer.pad_token_id), # key_input + Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # tilte_segment + Pad(axis=0, pad_val=0), # attention_mask + ): [data for data in fn(samples)] + + infer_data_loader = create_dataloader( + infer_ds, + mode="predict", + batch_size=args.batch_size, + batchify_fn=batchify_fn, + trans_fn=trans_func + ) + + cosin_sim = do_infer(model, tokenizer, infer_data_loader) + + with open(args.save_infer_path, "w", encoding="utf-8") as f: + for idx, cos in enumerate(cosin_sim): + msg = "{} --> {}\n".format(idx, cos) + f.write(msg) + + end_time = time.time() + print("running time {} s".format(end_time - start_time)) + + + diff --git a/examples/text_matching/diffcse/run_eval.sh b/examples/text_matching/diffcse/run_eval.sh new file mode 100644 index 000000000000..f00ab73a1de2 --- /dev/null +++ b/examples/text_matching/diffcse/run_eval.sh @@ -0,0 +1,19 @@ +export CUDA_VISIBLE_DEVICES=0 + +python run_diffcse.py \ + --mode "eval" \ + --extractor_name "rocketqa-zh-dureader-query-encoder" \ + --generator_name "ernie-1.0" \ + --discriminator_name "ernie-1.0" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --eval_set_file ./data/test_v1.txt \ + --ckpt_dir ./checkpoints/checkpoint_10000 \ + --batch_size "8" \ + --dropout "0.1" \ + --margin "0.0" \ + --scale "20" \ + --dup_rate "0.0" \ + --seed "0" \ + --device "gpu" + diff --git a/examples/text_matching/diffcse/run_infer.sh b/examples/text_matching/diffcse/run_infer.sh new file mode 100644 index 000000000000..9914aa894b04 --- /dev/null +++ b/examples/text_matching/diffcse/run_infer.sh @@ -0,0 +1,20 @@ +export CUDA_VISIBLE_DEVICES=0 + + +python run_diffcse.py \ + --mode "infer" \ + --extractor_name "rocketqa-zh-dureader-query-encoder" \ + --generator_name "ernie-1.0" \ + --discriminator_name "ernie-1.0" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --infer_set_file "./data/test.txt" \ + --ckpt_dir "./checkpoints/checkpoint_10000" \ + --save_infer_path "./infer_result.txt" \ + --batch_size "8" \ + --dropout "0.1" \ + --margin "0.0" \ + --scale "20" \ + --dup_rate "0.0" \ + --seed "0" \ + --device "gpu" diff --git a/examples/text_matching/diffcse/run_train.sh b/examples/text_matching/diffcse/run_train.sh new file mode 100644 index 000000000000..8fa347e70265 --- /dev/null +++ b/examples/text_matching/diffcse/run_train.sh @@ -0,0 +1,28 @@ +gpu_ids=0,1,2,3,4,5,6,7 +export CUDA_VISIBLE_DEVICES=${gpu_ids} + + +python -u -m paddle.distributed.launch --gpus ${gpu_ids} \ + run_diffcse.py \ + --mode "train" \ + --extractor_name "rocketqa-zh-dureader-query-encoder" \ + --generator_name "ernie-1.0" \ + --discriminator_name "ernie-1.0" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --train_set_file ./data/train.txt \ + --eval_set_file ./data/test_v1.txt \ + --save_dir ./checkpoints \ + --save_steps "10000" \ + --eval_steps "1000" \ + --batch_size "32" \ + --epochs "5" \ + --learning_rate "3e-5" \ + --weight_decay "0.01" \ + --warmup_proportion "0.01" \ + --dropout "0.1" \ + --margin "0.0" \ + --scale "20" \ + --dup_rate "0.0" \ + --seed "0" \ + --device "gpu" \ No newline at end of file diff --git a/examples/text_matching/diffcse/test_train.sh b/examples/text_matching/diffcse/test_train.sh new file mode 100644 index 000000000000..3d4c1f2c09fc --- /dev/null +++ b/examples/text_matching/diffcse/test_train.sh @@ -0,0 +1,25 @@ +export CUDA_VISIBLE_DEVICES=0 + +python run_diffcse.py \ + --mode "train" \ + --extractor_name "rocketqa-zh-dureader-query-encoder" \ + --generator_name "ernie-3.0-mini-zh" \ + --discriminator_name "ernie-3.0-base-zh" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --train_set_file ./data/train.txt \ + --eval_set_file ./data/test_v1.txt \ + --save_dir ./checkpoints \ + --save_steps "10000" \ + --eval_steps "10" \ + --batch_size "2" \ + --epochs "5" \ + --learning_rate "3e-5" \ + --weight_decay "0.01" \ + --warmup_proportion "0.01" \ + --dropout "0.1" \ + --margin "0.0" \ + --scale "20" \ + --dup_rate "0.0" \ + --seed "0" \ + --device "gpu" diff --git a/examples/text_matching/diffcse/utils.py b/examples/text_matching/diffcse/utils.py new file mode 100644 index 000000000000..8ef350bac183 --- /dev/null +++ b/examples/text_matching/diffcse/utils.py @@ -0,0 +1,28 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import random +import numpy as np + +import paddle + +def set_seed(seed=0): + random.seed(seed) + np.random.seed(seed) + paddle.seed(seed) + +def masked_fill(x, mask, value): + y = paddle.full(x.shape, value, x.dtype) + return paddle.where(mask, y, x) From d65208e53edb85d0c98275aa8ce5f58adaffdf7c Mon Sep 17 00:00:00 2001 From: milen <1649759610@qq.com> Date: Fri, 24 Jun 2022 13:49:31 +0000 Subject: [PATCH 002/116] refine readme --- examples/text_matching/diffcse/README.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/examples/text_matching/diffcse/README.md b/examples/text_matching/diffcse/README.md index 1662d3554719..50507a0fc79e 100644 --- a/examples/text_matching/diffcse/README.md +++ b/examples/text_matching/diffcse/README.md @@ -109,7 +109,10 @@ python run_diffcse.py \ --seed "0" \ --device "gpu" ``` +可支持配置的参数： +* `ckpt_dir`: 用于指定进行模型评估的checkpoint路径。 +其他参数解释同上。 ### 基于动态图模型预测 @@ -146,6 +149,12 @@ python run_diffcse.py \ ``` +可支持配置的参数： +* `infer_set_file`: 可选，用于指定测试集的路径。 +* `save_infer_path`: 可选，用于保存模型预测结果的文件路径。 + +其他参数解释同上。 + ## Reference [1] [1] Chuang Y S , Dangovski R , Luo H , et al. DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings[J]. arXiv e-prints, 2022. https://arxiv.org/pdf/2204.10298.pdf. \ No newline at end of file From ac4d64462526584871bd56408e3cbc603ddfe18f Mon Sep 17 00:00:00 2001 From: milen <1649759610@qq.com> Date: Fri, 24 Jun 2022 13:51:38 +0000 Subject: [PATCH 003/116] refine codestyle --- examples/text_matching/diffcse/README.md | 106 ++++---- .../text_matching/diffcse/custom_ernie.py | 3 +- examples/text_matching/diffcse/data.py | 68 +++-- .../text_matching/diffcse/eval_metrics.py | 87 ++++--- examples/text_matching/diffcse/model.py | 241 ++++++++++-------- examples/text_matching/diffcse/run_diffcse.py | 231 ++++++++++------- examples/text_matching/diffcse/utils.py | 3 +- 7 files changed, 427 insertions(+), 312 deletions(-) diff --git a/examples/text_matching/diffcse/README.md b/examples/text_matching/diffcse/README.md index 50507a0fc79e..502a696fc638 100644 --- a/examples/text_matching/diffcse/README.md +++ b/examples/text_matching/diffcse/README.md @@ -27,28 +27,28 @@ DiffCSE/ export CUDA_VISIBLE_DEVICES=0,1,2,3 python -u -m paddle.distributed.launch --gpus ${gpu_ids} \ - run_diffcse.py \ - --mode "train" \ - --extractor_name "rocketqa-zh-dureader-query-encoder" \ - --generator_name "ernie-1.0" \ - --discriminator_name "ernie-1.0" \ - --max_seq_length "128" \ - --output_emb_size "32" \ - --train_set_file ./data/train.txt \ - --eval_set_file ./data/test_v1.txt \ - --save_dir ./checkpoints \ - --save_steps "10000" \ - --eval_steps "1000" \ - --batch_size "32" \ - --epochs "3" \ - --learning_rate "3e-5" \ - --weight_decay "0.01" \ - --warmup_proportion "0.01" \ - --dropout "0.1" \ - --margin "0.0" \ - --scale "20" \ - --seed "0" \ - --device "gpu" + run_diffcse.py \ + --mode "train" \ + --extractor_name "rocketqa-zh-dureader-query-encoder" \ + --generator_name "ernie-1.0" \ + --discriminator_name "ernie-1.0" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --train_set_file ./data/train.txt \ + --eval_set_file ./data/test_v1.txt \ + --save_dir ./checkpoints \ + --save_steps "10000" \ + --eval_steps "1000" \ + --batch_size "32" \ + --epochs "3" \ + --learning_rate "3e-5" \ + --weight_decay "0.01" \ + --warmup_proportion "0.01" \ + --dropout "0.1" \ + --margin "0.0" \ + --scale "20" \ + --seed "0" \ + --device "gpu" ``` 可支持配置的参数： @@ -93,21 +93,21 @@ checkpoints/ export CUDA_VISIBLE_DEVICES=0 python run_diffcse.py \ - --mode "eval" \ - --extractor_name "rocketqa-zh-dureader-query-encoder" \ - --generator_name "ernie-1.0" \ - --discriminator_name "ernie-1.0" \ - --max_seq_length "128" \ - --output_emb_size "32" \ - --eval_set_file ./data/test_v1.txt \ - --ckpt_dir ./checkpoints/checkpoint_10000 \ - --batch_size "8" \ - --dropout "0.1" \ - --margin "0.0" \ - --scale "20" \ - --dup_rate "0.0" \ - --seed "0" \ - --device "gpu" + --mode "eval" \ + --extractor_name "rocketqa-zh-dureader-query-encoder" \ + --generator_name "ernie-1.0" \ + --discriminator_name "ernie-1.0" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --eval_set_file ./data/test_v1.txt \ + --ckpt_dir ./checkpoints/checkpoint_10000 \ + --batch_size "8" \ + --dropout "0.1" \ + --margin "0.0" \ + --scale "20" \ + --dup_rate "0.0" \ + --seed "0" \ + --device "gpu" ``` 可支持配置的参数： * `ckpt_dir`: 用于指定进行模型评估的checkpoint路径。 @@ -130,22 +130,22 @@ python run_diffcse.py \ export CUDA_VISIBLE_DEVICES=0 python run_diffcse.py \ - --mode "eval" \ - --extractor_name "rocketqa-zh-dureader-query-encoder" \ - --generator_name "ernie-1.0" \ - --discriminator_name "ernie-3.0-base-zh" \ - --max_seq_length "128" \ - --output_emb_size "32" \ - --infer_set_file "./data/test.txt" \ - --ckpt_dir "./checkpoints/best_spearman" \ + --mode "eval" \ + --extractor_name "rocketqa-zh-dureader-query-encoder" \ + --generator_name "ernie-1.0" \ + --discriminator_name "ernie-3.0-base-zh" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --infer_set_file "./data/test.txt" \ + --ckpt_dir "./checkpoints/best_spearman" \ --save_infer_path "./infer_result.txt" \ - --batch_size "8" \ - --dropout "0.1" \ - --margin "0.0" \ - --scale "20" \ - --dup_rate "0.0" \ - --seed "0" \ - --device "gpu" + --batch_size "8" \ + --dropout "0.1" \ + --margin "0.0" \ + --scale "20" \ + --dup_rate "0.0" \ + --seed "0" \ + --device "gpu" ``` @@ -157,4 +157,4 @@ python run_diffcse.py \ ## Reference -[1] [1] Chuang Y S , Dangovski R , Luo H , et al. DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings[J]. arXiv e-prints, 2022. https://arxiv.org/pdf/2204.10298.pdf. \ No newline at end of file +[1] [1] Chuang Y S , Dangovski R , Luo H , et al. DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings[J]. arXiv e-prints, 2022. https://arxiv.org/pdf/2204.10298.pdf. diff --git a/examples/text_matching/diffcse/custom_ernie.py b/examples/text_matching/diffcse/custom_ernie.py index 0c0c910feb38..d31e67ab523c 100644 --- a/examples/text_matching/diffcse/custom_ernie.py +++ b/examples/text_matching/diffcse/custom_ernie.py @@ -648,7 +648,8 @@ def forward(self, task_type_ids=task_type_ids) if cls_input is not None: - embedding_output = paddle.concat([cls_input.unsqueeze(1), embedding_output[:, 1:, :]], axis=1) + embedding_output = paddle.concat( + [cls_input.unsqueeze(1), embedding_output[:, 1:, :]], axis=1) encoder_outputs = self.encoder(embedding_output, attention_mask) sequence_output = encoder_outputs diff --git a/examples/text_matching/diffcse/data.py b/examples/text_matching/diffcse/data.py index 3dae95d40606..b2535262e99d 100644 --- a/examples/text_matching/diffcse/data.py +++ b/examples/text_matching/diffcse/data.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - import paddle import os @@ -23,25 +22,39 @@ def get_special_tokens(): return ["[PAD]", "[CLS]", "[SEP]", "[MASK]", "[UNK]"] + def get_special_token_ids(tokenizer): special_tokens = ["[PAD]", "[CLS]", "[SEP]", "[MASK]", "[UNK]"] return tokenizer.convert_tokens_to_ids(special_tokens) + def get_special_token_dict(tokenizer): special_tokens = ["[PAD]", "[CLS]", "[SEP]", "[MASK]", "[UNK]"] - special_token_dict = dict(zip(special_tokens, tokenizer.convert_tokens_to_ids(special_tokens))) + special_token_dict = dict( + zip(special_tokens, tokenizer.convert_tokens_to_ids(special_tokens))) return special_token_dict -def create_dataloader(dataset, mode="train", batch_size=1, batchify_fn=None, trans_fn=None): +def create_dataloader(dataset, + mode="train", + batch_size=1, + batchify_fn=None, + trans_fn=None): if trans_fn: dataset = dataset.map(trans_fn) shuffle = True if mode == "train" else False if mode == "train": - batch_sampler = paddle.io.DistributedBatchSampler(dataset, batch_size=batch_size, shuffle=shuffle) + batch_sampler = paddle.io.DistributedBatchSampler(dataset, + batch_size=batch_size, + shuffle=shuffle) else: - batch_sampler = paddle.io.BatchSampler(dataset, batch_size=batch_size, shuffle=shuffle) - return paddle.io.DataLoader(dataset=dataset, batch_sampler=batch_sampler, collate_fn=batchify_fn, return_list=True) + batch_sampler = paddle.io.BatchSampler(dataset, + batch_size=batch_size, + shuffle=shuffle) + return paddle.io.DataLoader(dataset=dataset, + batch_sampler=batch_sampler, + collate_fn=batchify_fn, + return_list=True) def convert_example(example, tokenizer, max_seq_length=512, do_evalute=False): @@ -52,7 +65,9 @@ def convert_example(example, tokenizer, max_seq_length=512, do_evalute=False): result += [example["label"]] else: # do_train - encoded_inputs = tokenizer(text=text, max_seq_len=max_seq_length, return_attention_mask=True) + encoded_inputs = tokenizer(text=text, + max_seq_len=max_seq_length, + return_attention_mask=True) # print(encoded_inputs) input_ids = encoded_inputs["input_ids"] token_type_ids = encoded_inputs["token_type_ids"] @@ -67,10 +82,12 @@ def read_text_single(data_path): data = line.rstrip() yield {"text_a": data, "text_b": data} + def masked_fill(x, mask, value): y = paddle.full(x.shape, value, x.dtype) return paddle.where(mask, y, x) + def mask_tokens(batch_inputs, tokenizer, mlm_probability=0.15): """ Description: Mask input_ids for masked language modeling: 80% MASK, 10% random, 10% original @@ -81,25 +98,35 @@ def mask_tokens(batch_inputs, tokenizer, mlm_probability=0.15): # print(mlm_inputs) probability_matrix = paddle.full(mlm_inputs.shape, mlm_probability) # print(probability_matrix) - - special_tokens_mask = paddle.cast(paddle.zeros(mlm_inputs.shape), dtype=bool) + + special_tokens_mask = paddle.cast(paddle.zeros(mlm_inputs.shape), + dtype=bool) for special_token_id in get_special_token_ids(tokenizer): - special_tokens_mask |= (mlm_inputs==special_token_id) + special_tokens_mask |= (mlm_inputs == special_token_id) - - probability_matrix = masked_fill(probability_matrix, special_tokens_mask, 0.0) + probability_matrix = masked_fill(probability_matrix, special_tokens_mask, + 0.0) # print(probability_matrix) - masked_indices = paddle.cast(paddle.bernoulli(probability_matrix), dtype=bool) + masked_indices = paddle.cast(paddle.bernoulli(probability_matrix), + dtype=bool) mlm_labels = masked_fill(mlm_labels, ~masked_indices, -100) # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) - indices_replaced = paddle.cast(paddle.bernoulli(paddle.full(mlm_inputs.shape, 0.8)), dtype=bool) & masked_indices - mlm_inputs = masked_fill(mlm_inputs, indices_replaced, tokenizer.mask_token_id) + indices_replaced = paddle.cast(paddle.bernoulli( + paddle.full(mlm_inputs.shape, 0.8)), + dtype=bool) & masked_indices + mlm_inputs = masked_fill(mlm_inputs, indices_replaced, + tokenizer.mask_token_id) # 10% of the time, we replace masked input tokens with random word - indices_random = paddle.cast(paddle.bernoulli(paddle.full(mlm_inputs.shape, 0.5)) ,dtype=bool) & masked_indices & ~indices_replaced - random_words = paddle.randint(0, len(tokenizer), mlm_inputs.shape, dtype=mlm_inputs.dtype) + indices_random = paddle.cast( + paddle.bernoulli(paddle.full(mlm_inputs.shape, 0.5)), + dtype=bool) & masked_indices & ~indices_replaced + random_words = paddle.randint(0, + len(tokenizer), + mlm_inputs.shape, + dtype=mlm_inputs.dtype) mlm_inputs = paddle.where(indices_random, random_words, mlm_inputs) # The rest of the time (10% of the time) we keep the masked input tokens unchanged @@ -139,7 +166,8 @@ def word_repetition(input_ids, token_type_ids, dup_rate=0.32): if (actual_len > 5): dup_len = random.randint(a=0, b=max(2, int(dup_rate * actual_len))) # Skip cls and sep position - dup_word_index = random.sample(list(range(1, actual_len - 1)), k=dup_len) + dup_word_index = random.sample(list(range(1, actual_len - 1)), + k=dup_len) r_input_id = [] r_token_type_id = [] @@ -162,5 +190,5 @@ def word_repetition(input_ids, token_type_ids, dup_rate=0.32): repetitied_input_ids[batch_id] += [0] * pad_len repetitied_token_type_ids[batch_id] += [0] * pad_len - return paddle.to_tensor(repetitied_input_ids), paddle.to_tensor(repetitied_token_type_ids) - + return paddle.to_tensor(repetitied_input_ids), paddle.to_tensor( + repetitied_token_type_ids) diff --git a/examples/text_matching/diffcse/eval_metrics.py b/examples/text_matching/diffcse/eval_metrics.py index cf0b9287e271..bb102363109e 100644 --- a/examples/text_matching/diffcse/eval_metrics.py +++ b/examples/text_matching/diffcse/eval_metrics.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - import numpy as np from scipy import stats from sklearn import metrics @@ -26,16 +25,21 @@ def eval_metrics(labels, sims): eval_res["spearman_corr"] = spearman_corr print("Spearman corr --> {:.5f}\n".format(spearman_corr)) - labels_2_cls = np.where(np.greater_equal(labels, 3), np.ones_like(labels), np.zeros_like(labels)) + labels_2_cls = np.where(np.greater_equal(labels, 3), np.ones_like(labels), + np.zeros_like(labels)) - pre, rec, thr = metrics.precision_recall_curve(labels_2_cls, sims, pos_label=1) + pre, rec, thr = metrics.precision_recall_curve(labels_2_cls, + sims, + pos_label=1) pre_rec = np.sum([pre, rec], axis=0) best_pre_rec = np.max(pre_rec) best_pre = pre[np.argmax(pre_rec)] best_rec = rec[np.argmax(pre_rec)] best_thr = thr[np.argmax(pre_rec)] eval_res["best_pre_rec_thr"] = [best_pre_rec, best_pre, best_rec, best_thr] - print("2-CLS best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}".format(best_pre_rec, best_pre, best_rec, best_thr)) + print( + "2-CLS best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}" + .format(best_pre_rec, best_pre, best_rec, best_thr)) pre_rec_thr = list(zip(pre, rec, thr)) pre_rec_thr.sort(key=lambda x: x[1], reverse=True) @@ -46,20 +50,22 @@ def eval_metrics(labels, sims): pre_rec_thr_at_K = x break eval_res["best_pre_rec_thr_at_K"] = pre_rec_thr_at_K - print("2-CLS best pre@{:.2f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}".format(at_K, pre_rec_thr_at_K[0], pre_rec_thr_at_K[1], pre_rec_thr_at_K[2])) + print( + "2-CLS best pre@{:.2f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}". + format(at_K, pre_rec_thr_at_K[0], pre_rec_thr_at_K[1], + pre_rec_thr_at_K[2])) - preds_2_cls = np.where(np.greater_equal(sims, best_thr), np.ones_like(sims), np.zeros_like(sims)) + preds_2_cls = np.where(np.greater_equal(sims, best_thr), np.ones_like(sims), + np.zeros_like(sims)) acc_2_cls = metrics.accuracy_score(labels_2_cls, preds_2_cls) auc_2_cls = metrics.roc_auc_score(labels_2_cls, sims) - report_2_cls = metrics.classification_report( - labels_2_cls, - preds_2_cls, - labels=[0, 1], - target_names=["0", "1"], - digits=5, - output_dict=True - ) + report_2_cls = metrics.classification_report(labels_2_cls, + preds_2_cls, + labels=[0, 1], + target_names=["0", "1"], + digits=5, + output_dict=True) eval_res["acc_2_cls"] = acc_2_cls eval_res["auc_2_cls"] = auc_2_cls eval_res["report_2_cls"] = report_2_cls @@ -67,21 +73,32 @@ def eval_metrics(labels, sims): print("2-CLS accuracy --> {}".format(acc_2_cls)) print("2-CLS roc-auc --> {}".format(auc_2_cls)) print("\t".join(["label", "precision", "recall", "f1-score", "support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("2-CLS 0", report_2_cls["0"]["precision"], report_2_cls["0"]["recall"], report_2_cls["0"]["f1-score"], report_2_cls["0"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}\n".format("2-CLS 1", report_2_cls["1"]["precision"], report_2_cls["1"]["recall"], report_2_cls["1"]["f1-score"], report_2_cls["1"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( + "2-CLS 0", report_2_cls["0"]["precision"], report_2_cls["0"]["recall"], + report_2_cls["0"]["f1-score"], report_2_cls["0"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}\n".format( + "2-CLS 1", report_2_cls["1"]["precision"], report_2_cls["1"]["recall"], + report_2_cls["1"]["f1-score"], report_2_cls["1"]["support"])) best_thr_6_cls = [] for pivot in [5, 4, 3, 2, 1]: - labels_pivot = np.where(np.greater_equal(labels, pivot), np.ones_like(labels), np.zeros_like(labels)) - pre_pivot, rec_pivot, thr_pivot = metrics.precision_recall_curve(labels_pivot, sims, pos_label=1) + labels_pivot = np.where(np.greater_equal(labels, pivot), + np.ones_like(labels), np.zeros_like(labels)) + pre_pivot, rec_pivot, thr_pivot = metrics.precision_recall_curve( + labels_pivot, sims, pos_label=1) pre_rec_pivot = np.sum([pre_pivot, rec_pivot], axis=0) best_pre_rec_pivot = np.max(pre_rec_pivot) best_pre_pivot = pre_pivot[np.argmax(pre_rec_pivot)] best_rec_pivot = rec_pivot[np.argmax(pre_rec_pivot)] best_thr_pivot = thr_pivot[np.argmax(pre_rec_pivot)] best_thr_6_cls.append(best_thr_pivot) - eval_res["best_pre_rec_thr_6_cls_at_{}_pivot".format(pivot)] = [best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, best_thr_pivot] - print("6-CLS [split at {} pivot] best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}".format(pivot, best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, best_thr_pivot)) + eval_res["best_pre_rec_thr_6_cls_at_{}_pivot".format(pivot)] = [ + best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, best_thr_pivot + ] + print( + "6-CLS [split at {} pivot] best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}" + .format(pivot, best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, + best_thr_pivot)) preds = [] for s in sims: @@ -99,19 +116,31 @@ def eval_metrics(labels, sims): labels=[0, 1, 2, 3, 4, 5], target_names=["0", "1", "2", "3", "4", "5"], digits=5, - output_dict=True - ) + output_dict=True) eval_res["acc_6_cls"] = acc_6_cls eval_res["report_6_cls"] = report_6_cls - print("6-CLS metrics are evaluated on the best thr --> {}".format(best_thr_6_cls)) + print("6-CLS metrics are evaluated on the best thr --> {}".format( + best_thr_6_cls)) print("6-CLS accuracy --> {}".format(acc_6_cls)) print("\t".join(["label", "precision", "recall", "f1-score", "support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 0", report_6_cls["0"]["precision"], report_6_cls["0"]["recall"], report_6_cls["0"]["f1-score"], report_6_cls["0"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 1", report_6_cls["1"]["precision"], report_6_cls["1"]["recall"], report_6_cls["1"]["f1-score"], report_6_cls["1"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 2", report_6_cls["2"]["precision"], report_6_cls["2"]["recall"], report_6_cls["2"]["f1-score"], report_6_cls["2"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 3", report_6_cls["3"]["precision"], report_6_cls["3"]["recall"], report_6_cls["3"]["f1-score"], report_6_cls["3"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 4", report_6_cls["4"]["precision"], report_6_cls["4"]["recall"], report_6_cls["4"]["f1-score"], report_6_cls["4"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 5", report_6_cls["5"]["precision"], report_6_cls["5"]["recall"], report_6_cls["5"]["f1-score"], report_6_cls["5"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( + "6-CLS 0", report_6_cls["0"]["precision"], report_6_cls["0"]["recall"], + report_6_cls["0"]["f1-score"], report_6_cls["0"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( + "6-CLS 1", report_6_cls["1"]["precision"], report_6_cls["1"]["recall"], + report_6_cls["1"]["f1-score"], report_6_cls["1"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( + "6-CLS 2", report_6_cls["2"]["precision"], report_6_cls["2"]["recall"], + report_6_cls["2"]["f1-score"], report_6_cls["2"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( + "6-CLS 3", report_6_cls["3"]["precision"], report_6_cls["3"]["recall"], + report_6_cls["3"]["f1-score"], report_6_cls["3"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( + "6-CLS 4", report_6_cls["4"]["precision"], report_6_cls["4"]["recall"], + report_6_cls["4"]["f1-score"], report_6_cls["4"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( + "6-CLS 5", report_6_cls["5"]["precision"], report_6_cls["5"]["recall"], + report_6_cls["5"]["f1-score"], report_6_cls["5"]["support"])) return eval_res diff --git a/examples/text_matching/diffcse/model.py b/examples/text_matching/diffcse/model.py index d580dc57729e..320db25cbbad 100644 --- a/examples/text_matching/diffcse/model.py +++ b/examples/text_matching/diffcse/model.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - import numpy as np import paddle @@ -25,23 +24,38 @@ class Extractor(nn.Layer): - def __init__(self, pretrained_model_name, dropout=None, margin=0.0, scale=20, output_emb_size=None): + + def __init__(self, + pretrained_model_name, + dropout=None, + margin=0.0, + scale=20, + output_emb_size=None): super().__init__() self.ptm = AutoModel.from_pretrained(pretrained_model_name) self.dropout = nn.Dropout(dropout if dropout is not None else 0.1) # if output_emb_size is greater than 0, then add Linear layer to reduce embedding_size self.output_emb_size = output_emb_size if output_emb_size > 0: - weight_attr = paddle.ParamAttr(initializer=paddle.nn.initializer.TruncatedNormal(std=0.02)) - self.emb_reduce_linear = paddle.nn.Linear(768, output_emb_size, weight_attr=weight_attr) + weight_attr = paddle.ParamAttr( + initializer=paddle.nn.initializer.TruncatedNormal(std=0.02)) + self.emb_reduce_linear = paddle.nn.Linear(768, + output_emb_size, + weight_attr=weight_attr) self.margin = margin # Used scaling cosine similarity to ease converge self.scale = scale - def get_pooled_embedding(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, with_pooler=False): + def get_pooled_embedding(self, + input_ids, + token_type_ids=None, + position_ids=None, + attention_mask=None, + with_pooler=False): # Note: cls_embedding is poolerd embedding with act tanh - sequence_output, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids, attention_mask) + sequence_output, cls_embedding = self.ptm(input_ids, token_type_ids, + position_ids, attention_mask) if not with_pooler: ori_cls_embedding = sequence_output[:, 0, :] if self.output_emb_size > 0: @@ -51,70 +65,63 @@ def get_pooled_embedding(self, input_ids, token_type_ids=None, position_ids=None cls_embedding = F.normalize(cls_embedding, p=2, axis=-1) return cls_embedding, ori_cls_embedding - def cosine_sim( - self, - query_input_ids, - key_input_ids, - query_token_type_ids=None, - query_position_ids=None, - query_attention_mask=None, - key_token_type_ids=None, - key_position_ids=None, - key_attention_mask=None, - with_pooler=False - ): + def cosine_sim(self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + query_position_ids=None, + query_attention_mask=None, + key_token_type_ids=None, + key_position_ids=None, + key_attention_mask=None, + with_pooler=False): query_cls_embedding, _ = self.get_pooled_embedding( query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask, - with_pooler=with_pooler - ) + with_pooler=with_pooler) key_cls_embedding, _ = self.get_pooled_embedding( key_input_ids, key_token_type_ids, key_position_ids, key_attention_mask, - with_pooler=with_pooler - ) + with_pooler=with_pooler) - cosine_sim = paddle.sum(query_cls_embedding * key_cls_embedding, axis=-1) + cosine_sim = paddle.sum(query_cls_embedding * key_cls_embedding, + axis=-1) return cosine_sim - def forward( - self, - query_input_ids, - key_input_ids, - query_token_type_ids=None, - query_position_ids=None, - query_attention_mask=None, - key_token_type_ids=None, - key_position_ids=None, - key_attention_mask=None, - with_pooler=False - ): + def forward(self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + query_position_ids=None, + query_attention_mask=None, + key_token_type_ids=None, + key_position_ids=None, + key_attention_mask=None, + with_pooler=False): query_cls_embedding, ori_query_cls_embedding = self.get_pooled_embedding( query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask, - with_pooler=with_pooler - ) + with_pooler=with_pooler) key_cls_embedding, ori_key_cls_embedding = self.get_pooled_embedding( key_input_ids, key_token_type_ids, key_position_ids, key_attention_mask, - with_pooler=with_pooler - ) - cosine_sim = paddle.matmul(query_cls_embedding, key_cls_embedding, transpose_y=True) + with_pooler=with_pooler) + cosine_sim = paddle.matmul(query_cls_embedding, + key_cls_embedding, + transpose_y=True) # substract margin from all positive samples cosine_sim() - margin_diag = paddle.full( - shape=[query_cls_embedding.shape[0]], - fill_value=self.margin, - dtype=paddle.get_default_dtype() - ) + margin_diag = paddle.full(shape=[query_cls_embedding.shape[0]], + fill_value=self.margin, + dtype=paddle.get_default_dtype()) cosine_sim = cosine_sim - paddle.diag(margin_diag) @@ -125,17 +132,28 @@ def forward( labels = paddle.reshape(labels, shape=[-1, 1]) loss = F.cross_entropy(input=cosine_sim, label=labels) - ori_cls_embedding = paddle.concat([ori_query_cls_embedding, ori_key_cls_embedding], axis=0) + ori_cls_embedding = paddle.concat( + [ori_query_cls_embedding, ori_key_cls_embedding], axis=0) return loss, ori_cls_embedding + class Discriminator(nn.Layer): + def __init__(self, ptm_model_name): super(Discriminator, self).__init__() self.ptm = CustomErnie.from_pretrained(ptm_model_name) self.classifier = nn.Linear(self.ptm.config["hidden_size"], 2) - - def forward(self, input_ids, labels, cls_input, token_type_ids=None, attention_mask=None): - sequence_output, _ = self.ptm(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, cls_input=cls_input) + + def forward(self, + input_ids, + labels, + cls_input, + token_type_ids=None, + attention_mask=None): + sequence_output, _ = self.ptm(input_ids, + token_type_ids=token_type_ids, + attention_mask=attention_mask, + cls_input=cls_input) pred_scores = self.classifier(sequence_output) loss = F.cross_entropy(input=pred_scores, label=labels) @@ -143,6 +161,7 @@ def forward(self, input_ids, labels, cls_input, token_type_ids=None, attention_m class DiffCSE(nn.Layer): + def __init__(self, args, tokenizer): super(DiffCSE, self).__init__() self.args = args @@ -151,95 +170,95 @@ def __init__(self, args, tokenizer): self.generator_name = args.generator_name self.discriminator_name = args.discriminator_name - self.extractor = Extractor(args.extractor_name, margin=args.margin, scale=args.scale, output_emb_size=args.output_emb_size) + self.extractor = Extractor(args.extractor_name, + margin=args.margin, + scale=args.scale, + output_emb_size=args.output_emb_size) self.generator = ErnieForMaskedLM.from_pretrained(args.generator_name) self.discriminator = Discriminator(args.discriminator_name) - def train_forward(self, - query_input_ids, - key_input_ids, - query_token_type_ids=None, - key_token_type_ids=None, - query_attention_mask=None, - key_attention_mask=None, - cls_token = 1 - ): + def train_forward(self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + key_token_type_ids=None, + query_attention_mask=None, + key_attention_mask=None, + cls_token=1): # extract senmantic vector with extractor and then comput CL loss - loss, ori_cls_embedding = self.extractor(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask) + loss, ori_cls_embedding = self.extractor( + query_input_ids, + key_input_ids, + query_token_type_ids=query_token_type_ids, + key_token_type_ids=key_token_type_ids, + query_attention_mask=query_attention_mask, + key_attention_mask=key_attention_mask) - with paddle.no_grad(): # mask tokens for query and key input_ids and then predict mask token with generator input_ids = paddle.concat([query_input_ids, key_input_ids], axis=0) - attention_mask = paddle.concat([query_attention_mask, key_attention_mask], axis=0) - mlm_input_ids, _ = mask_tokens(paddle.concat([query_input_ids, key_input_ids], axis=0), self.tokenizer, mlm_probability=self.args.mlm_probability) - pred_tokens = self.generator(mlm_input_ids, attention_mask=attention_mask).argmax(-1) + attention_mask = paddle.concat( + [query_attention_mask, key_attention_mask], axis=0) + mlm_input_ids, _ = mask_tokens( + paddle.concat([query_input_ids, key_input_ids], axis=0), + self.tokenizer, + mlm_probability=self.args.mlm_probability) + pred_tokens = self.generator( + mlm_input_ids, attention_mask=attention_mask).argmax(-1) pred_tokens[:, 0] = cls_token e_inputs = pred_tokens * attention_mask replaced = pred_tokens != input_ids e_labels = paddle.cast(replaced, dtype="int64") * attention_mask - rtd_loss = self.discriminator(e_inputs, e_labels, cls_input=ori_cls_embedding) + rtd_loss = self.discriminator(e_inputs, + e_labels, + cls_input=ori_cls_embedding) loss = loss + self.args.lambda_weight * rtd_loss return loss - - def test_forward(self, - query_input_ids, - key_input_ids, - query_token_type_ids=None, - key_token_type_ids=None, - query_attention_mask=None, - key_attention_mask=None - ): + def test_forward(self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + key_token_type_ids=None, + query_attention_mask=None, + key_attention_mask=None): # compute cosine similarity for query and key text - cos_sim = self.extractor.cosine_sim(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask) + cos_sim = self.extractor.cosine_sim( + query_input_ids, + key_input_ids, + query_token_type_ids=query_token_type_ids, + key_token_type_ids=key_token_type_ids, + query_attention_mask=query_attention_mask, + key_attention_mask=key_attention_mask) return cos_sim - def forward(self, - query_input_ids, + def forward(self, + query_input_ids, key_input_ids, - query_token_type_ids=None, + query_token_type_ids=None, key_token_type_ids=None, query_attention_mask=None, key_attention_mask=None, - cls_token = 1, - mode="train" - ): + cls_token=1, + mode="train"): if mode == "train": - return self.train_forward(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask, cls_token=cls_token) + return self.train_forward(query_input_ids, + key_input_ids, + query_token_type_ids=query_token_type_ids, + key_token_type_ids=key_token_type_ids, + query_attention_mask=query_attention_mask, + key_attention_mask=key_attention_mask, + cls_token=cls_token) else: - return self.test_forward(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask) - - - - - - - - - - - - - - - - - - - - - - - - - - - - + return self.test_forward(query_input_ids, + key_input_ids, + query_token_type_ids=query_token_type_ids, + key_token_type_ids=key_token_type_ids, + query_attention_mask=query_attention_mask, + key_attention_mask=key_attention_mask) diff --git a/examples/text_matching/diffcse/run_diffcse.py b/examples/text_matching/diffcse/run_diffcse.py index c2346b3031ac..9d47b3eea488 100644 --- a/examples/text_matching/diffcse/run_diffcse.py +++ b/examples/text_matching/diffcse/run_diffcse.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - import os import time import random @@ -33,7 +32,6 @@ from eval_metrics import eval_metrics from data import read_text_single, read_text_pair, convert_example, create_dataloader, word_repetition - # yapf: disable parser = argparse.ArgumentParser() parser.add_argument("--mode", choices=["train", "eval", "infer"], default="infer", help="Select which mode to run model, defaults to infer.") @@ -69,7 +67,9 @@ def do_infer(model, tokenizer, data_loader): - assert isinstance(model, Extractor), "please make sure that model is instance of Extractor." + assert isinstance( + model, + Extractor), "please make sure that model is instance of Extractor." sims = [] model.eval() with paddle.no_grad(): @@ -82,7 +82,7 @@ def do_infer(model, tokenizer, data_loader): key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask, - ) + ) sims.append(cosine_sim.numpy()) sims = np.concatenate(sims, axis=0) model.train() @@ -90,7 +90,9 @@ def do_infer(model, tokenizer, data_loader): def do_eval(model, tokenizer, data_loader): - assert isinstance(model, Extractor), "please make sure that model is instance of Extractor." + assert isinstance( + model, + Extractor), "please make sure that model is instance of Extractor." total_num = 0 spearman_corr = 0.0 sims = [] @@ -107,7 +109,7 @@ def do_eval(model, tokenizer, data_loader): key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask, - ) + ) sims.append(cosine_sim.numpy()) labels.append(label.numpy()) @@ -118,28 +120,27 @@ def do_eval(model, tokenizer, data_loader): return eval_res, total_num - def do_train(model, tokenizer, train_data_loader, dev_data_loader): - num_training_steps = args.max_steps if args.max_steps > 0 else len(train_data_loader) * args.epochs + num_training_steps = args.max_steps if args.max_steps > 0 else len( + train_data_loader) * args.epochs - lr_scheduler = LinearDecayWithWarmup( - args.learning_rate, - num_training_steps, - args.warmup_proportion - ) + lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps, + args.warmup_proportion) - decay_params = [p.name for n, p in model.named_parameters() if not any(nd in n for nd in ["bias", "norm"])] + decay_params = [ + p.name for n, p in model.named_parameters() + if not any(nd in n for nd in ["bias", "norm"]) + ] optimizer = paddle.optimizer.AdamW( learning_rate=lr_scheduler, parameters=model.parameters(), weight_decay=args.weight_decay, - apply_decay_param_fun=lambda x: x in decay_params - ) + apply_decay_param_fun=lambda x: x in decay_params) global_step = 0 best_spearman = 0.0 best_pre_rec = 0.0 - best_rec95 = 0.0 + best_rec95 = 0.0 tic_train = time.time() model = paddle.DataParallel(model) model.train() @@ -147,76 +148,106 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader): for step, batch in enumerate(train_data_loader, start=1): query_input_ids, query_token_type_ids, query_attention_mask, key_input_ids, key_token_type_ids, key_attention_mask = batch - if(args.dup_rate > 0): - query_input_ids, query_token_type_ids = word_repetition(query_input_ids, query_token_type_ids, args.dup_rate) - key_input_ids, key_token_type_ids = word_repetition(key_input_ids, key_token_type_ids, args.dup_rate) + if (args.dup_rate > 0): + query_input_ids, query_token_type_ids = word_repetition( + query_input_ids, query_token_type_ids, args.dup_rate) + key_input_ids, key_token_type_ids = word_repetition( + key_input_ids, key_token_type_ids, args.dup_rate) - loss = model( - query_input_ids, - key_input_ids, - query_token_type_ids=query_token_type_ids, - key_token_type_ids=key_token_type_ids, - query_attention_mask=query_attention_mask, - key_attention_mask=key_attention_mask, - cls_token=tokenizer.cls_token_id - ) + loss = model(query_input_ids, + key_input_ids, + query_token_type_ids=query_token_type_ids, + key_token_type_ids=key_token_type_ids, + query_attention_mask=query_attention_mask, + key_attention_mask=key_attention_mask, + cls_token=tokenizer.cls_token_id) global_step += 1 if global_step % (args.eval_steps // 10) == 0 and rank == 0: - print("global step {}, epoch: {}, batch: {}, loss: {:.5f}, speed: {:.2f} step/s".format(global_step, epoch, step, loss.item(), (args.eval_steps // 10) / (time.time() - tic_train))) + print( + "global step {}, epoch: {}, batch: {}, loss: {:.5f}, speed: {:.2f} step/s" + .format(global_step, epoch, step, loss.item(), + (args.eval_steps // 10) / + (time.time() - tic_train))) tic_train = time.time() if global_step % args.eval_steps == 0 and rank == 0: - eval_res, total_num = do_eval(model._layers.extractor, tokenizer, dev_data_loader) + eval_res, total_num = do_eval(model._layers.extractor, + tokenizer, dev_data_loader) spearman = eval_res["spearman_corr"] if best_spearman < spearman: - print("best checkpoint has been updated: from last best_spearman {} --> new spearman {}.".format(best_spearman, spearman)) + print( + "best checkpoint has been updated: from last best_spearman {} --> new spearman {}." + .format(best_spearman, spearman)) best_spearman = spearman # save best model save_dir = os.path.join(args.save_dir, "best_spearman") if not os.path.exists(save_dir): os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, "model_state.pdparams") - paddle.save(model._layers.extractor.state_dict(), save_param_path) + save_param_path = os.path.join(save_dir, + "model_state.pdparams") + paddle.save(model._layers.extractor.state_dict(), + save_param_path) tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: - f.write(str(global_step)+" "+str(epoch)+ " "+ str(batch)) + with open(os.path.join(save_dir, "best_step.txt"), + "w", + encoding="utf-8") as f: + f.write( + str(global_step) + " " + str(epoch) + " " + + str(batch)) pre_rec = eval_res["best_pre_rec_thr"][0] if best_pre_rec < pre_rec: - print("best checkpoint has been updated: from last best_pre_rec {} --> new pre_rec {}.".format(best_pre_rec, pre_rec)) + print( + "best checkpoint has been updated: from last best_pre_rec {} --> new pre_rec {}." + .format(best_pre_rec, pre_rec)) best_pre_rec = pre_rec # save best model save_dir = os.path.join(args.save_dir, "best_pre_rec") if not os.path.exists(save_dir): os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, "model_state.pdparams") - paddle.save(model._layers.extractor.state_dict(), save_param_path) + save_param_path = os.path.join(save_dir, + "model_state.pdparams") + paddle.save(model._layers.extractor.state_dict(), + save_param_path) tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: - f.write(str(global_step)+" "+str(epoch)+ " "+ str(batch)) - + with open(os.path.join(save_dir, "best_step.txt"), + "w", + encoding="utf-8") as f: + f.write( + str(global_step) + " " + str(epoch) + " " + + str(batch)) + rec95 = eval_res["best_pre_rec_thr_at_K"][1] if best_rec95 < rec95: - print("best checkpoint has been updated: from last best_rec95 {} --> new rec95 {}.".format(best_rec95, rec95)) + print( + "best checkpoint has been updated: from last best_rec95 {} --> new rec95 {}." + .format(best_rec95, rec95)) best_rec95 = rec95 # save best model save_dir = os.path.join(args.save_dir, "best_rec95") if not os.path.exists(save_dir): os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, "model_state.pdparams") - paddle.save(model._layers.extractor.state_dict(), save_param_path) + save_param_path = os.path.join(save_dir, + "model_state.pdparams") + paddle.save(model._layers.extractor.state_dict(), + save_param_path) tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: - f.write(str(global_step)+" "+str(epoch)+ " "+ str(batch)) + with open(os.path.join(save_dir, "best_step.txt"), + "w", + encoding="utf-8") as f: + f.write( + str(global_step) + " " + str(epoch) + " " + + str(batch)) model.train() - + loss.backward() optimizer.step() lr_scheduler.step() optimizer.clear_grad() if global_step % args.save_steps == 0 and rank == 0: - save_dir = os.path.join(args.save_dir, "checkpoint_{}".format(global_step)) + save_dir = os.path.join(args.save_dir, + "checkpoint_{}".format(global_step)) if not os.path.exists(save_dir): os.makedirs(save_dir) save_param_path = os.path.join(save_dir, "model_state.pdparams") @@ -240,20 +271,23 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader): os.makedirs(args.save_dir) # define token for processing data - tokenizer = ppnlp.transformers.AutoTokenizer.from_pretrained(args.extractor_name) + tokenizer = ppnlp.transformers.AutoTokenizer.from_pretrained( + args.extractor_name) - trans_func = partial( - convert_example, - tokenizer=tokenizer, - max_seq_length=args.max_seq_length - ) + trans_func = partial(convert_example, + tokenizer=tokenizer, + max_seq_length=args.max_seq_length) if args.mode == "train": start_time = time.time() # intializing DiffCSE model model = DiffCSE(args, tokenizer) - train_ds = load_dataset(read_text_single, data_path=args.train_set_file, lazy=False) - dev_ds = load_dataset(read_text_pair, data_path=args.eval_set_file, lazy=False) + train_ds = load_dataset(read_text_single, + data_path=args.train_set_file, + lazy=False) + dev_ds = load_dataset(read_text_pair, + data_path=args.eval_set_file, + lazy=False) batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input @@ -273,40 +307,42 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader): Stack(dtype="int64"), # labels ): [data for data in fn(samples)] - train_data_loader = create_dataloader( - train_ds, - mode="train", - batch_size=args.batch_size, - batchify_fn=batchify_fn, - trans_fn=trans_func - ) - dev_data_loader = create_dataloader( - dev_ds, - mode="eval", - batch_size=args.batch_size, - batchify_fn=dev_batchify_fn, - trans_fn=trans_func - ) + train_data_loader = create_dataloader(train_ds, + mode="train", + batch_size=args.batch_size, + batchify_fn=batchify_fn, + trans_fn=trans_func) + dev_data_loader = create_dataloader(dev_ds, + mode="eval", + batch_size=args.batch_size, + batchify_fn=dev_batchify_fn, + trans_fn=trans_func) do_train(model, tokenizer, train_data_loader, dev_data_loader) end_time = time.time() print("running time {} s".format(end_time - start_time)) - if args.mode == "eval": start_time = time.time() # initalizing Extractor model for eval - model = Extractor(args.extractor_name, margin=args.margin, scale=args.scale, output_emb_size=args.output_emb_size) + model = Extractor(args.extractor_name, + margin=args.margin, + scale=args.scale, + output_emb_size=args.output_emb_size) # load model from saved checkpoint if args.ckpt_dir: init_from_ckpt = os.path.join(args.ckpt_dir, "model_state.pdparams") if os.path.isfile(init_from_ckpt): - print("*************************initializing model from {}*****************************".format(init_from_ckpt)) + print( + "*************************initializing model from {}*****************************" + .format(init_from_ckpt)) state_dict = paddle.load(init_from_ckpt) model.set_dict(state_dict) - dev_ds = load_dataset(read_text_pair, data_path=args.eval_set_file, lazy=False) + dev_ds = load_dataset(read_text_pair, + data_path=args.eval_set_file, + lazy=False) dev_batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input @@ -318,33 +354,39 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader): Stack(dtype="int64"), # labels ): [data for data in fn(samples)] - dev_data_loader = create_dataloader( - dev_ds, - mode="eval", - batch_size=args.batch_size, - batchify_fn=dev_batchify_fn, - trans_fn=trans_func - ) + dev_data_loader = create_dataloader(dev_ds, + mode="eval", + batch_size=args.batch_size, + batchify_fn=dev_batchify_fn, + trans_fn=trans_func) eval_res, total_num = do_eval(model, tokenizer, dev_data_loader) end_time = time.time() print("running time {} s".format(end_time - start_time)) - if args.mode == "infer": + if args.mode == "infer": start_time = time.time() # initalizing Extractor model for eval - model = Extractor(args.extractor_name, margin=args.margin, scale=args.scale, output_emb_size=args.output_emb_size) + model = Extractor(args.extractor_name, + margin=args.margin, + scale=args.scale, + output_emb_size=args.output_emb_size) # load model from saved checkpoint if args.ckpt_dir: init_from_ckpt = os.path.join(args.ckpt_dir, "model_state.pdparams") if os.path.isfile(init_from_ckpt): - print("*************************initializing model from {}*****************************".format(init_from_ckpt)) + print( + "*************************initializing model from {}*****************************" + .format(init_from_ckpt)) state_dict = paddle.load(init_from_ckpt) model.set_dict(state_dict) - infer_ds = load_dataset(read_text_pair, data_path=args.infer_set_file, lazy=False, is_infer=True) - + infer_ds = load_dataset(read_text_pair, + data_path=args.infer_set_file, + lazy=False, + is_infer=True) + batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # query_segment @@ -354,13 +396,11 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader): Pad(axis=0, pad_val=0), # attention_mask ): [data for data in fn(samples)] - infer_data_loader = create_dataloader( - infer_ds, - mode="predict", - batch_size=args.batch_size, - batchify_fn=batchify_fn, - trans_fn=trans_func - ) + infer_data_loader = create_dataloader(infer_ds, + mode="predict", + batch_size=args.batch_size, + batchify_fn=batchify_fn, + trans_fn=trans_func) cosin_sim = do_infer(model, tokenizer, infer_data_loader) @@ -371,6 +411,3 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader): end_time = time.time() print("running time {} s".format(end_time - start_time)) - - - diff --git a/examples/text_matching/diffcse/utils.py b/examples/text_matching/diffcse/utils.py index 8ef350bac183..6fa7dcb9fcdc 100644 --- a/examples/text_matching/diffcse/utils.py +++ b/examples/text_matching/diffcse/utils.py @@ -12,17 +12,18 @@ # See the License for the specific language governing permissions and # limitations under the License. - import random import numpy as np import paddle + def set_seed(seed=0): random.seed(seed) np.random.seed(seed) paddle.seed(seed) + def masked_fill(x, mask, value): y = paddle.full(x.shape, value, x.dtype) return paddle.where(mask, y, x) From 3f433b948ea77eaad645d643e7b2987bc731153b Mon Sep 17 00:00:00 2001 From: milen <1649759610@qq.com> Date: Fri, 24 Jun 2022 13:53:14 +0000 Subject: [PATCH 004/116] refine readme --- examples/text_matching/diffcse/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/text_matching/diffcse/README.md b/examples/text_matching/diffcse/README.md index 502a696fc638..c35ccccc2e23 100644 --- a/examples/text_matching/diffcse/README.md +++ b/examples/text_matching/diffcse/README.md @@ -157,4 +157,4 @@ python run_diffcse.py \ ## Reference -[1] [1] Chuang Y S , Dangovski R , Luo H , et al. DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings[J]. arXiv e-prints, 2022. https://arxiv.org/pdf/2204.10298.pdf. +[1] Chuang Y S , Dangovski R , Luo H , et al. DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings[J]. arXiv e-prints, 2022. https://arxiv.org/pdf/2204.10298.pdf. From d3f6adabfd4ad1fd8f676d39bf90d25c424db537 Mon Sep 17 00:00:00 2001 From: milen <1649759610@qq.com> Date: Fri, 24 Jun 2022 13:54:32 +0000 Subject: [PATCH 005/116] refine readme --- examples/text_matching/diffcse/README.md | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/examples/text_matching/diffcse/README.md b/examples/text_matching/diffcse/README.md index c35ccccc2e23..b13a5662d813 100644 --- a/examples/text_matching/diffcse/README.md +++ b/examples/text_matching/diffcse/README.md @@ -116,16 +116,7 @@ python run_diffcse.py \ ### 基于动态图模型预测 -测试数据示例如下，： -```text -谁有狂三这张高清的 这张高清图，谁有 -英雄联盟什么英雄最好 英雄联盟最好英雄是什么 -这是什么意思，被蹭网吗 我也是醉了，这是什么意思 -现在有什么动画片好看呢？ 现在有什么好看的动画片吗？ -请问晶达电子厂现在的工资待遇怎么样要求有哪些 三星电子厂工资待遇怎么样啊 -``` - -执行如下命令开始预测： +可以运行如下命令，进行模型预测： ```shell export CUDA_VISIBLE_DEVICES=0 From 54ed34bec7a9d3e89fc71dee5ebc027e8c779adb Mon Sep 17 00:00:00 2001 From: milen <1649759610@qq.com> Date: Sun, 26 Jun 2022 13:55:14 +0000 Subject: [PATCH 006/116] fix model saving bug --- examples/text_matching/diffcse/run_diffcse.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/text_matching/diffcse/run_diffcse.py b/examples/text_matching/diffcse/run_diffcse.py index 9d47b3eea488..9e3a47d9c10f 100644 --- a/examples/text_matching/diffcse/run_diffcse.py +++ b/examples/text_matching/diffcse/run_diffcse.py @@ -251,7 +251,8 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader): if not os.path.exists(save_dir): os.makedirs(save_dir) save_param_path = os.path.join(save_dir, "model_state.pdparams") - paddle.save(model.state_dict(), save_param_path) + paddle.save(model._layers.extractor.state_dict(), + save_param_path) tokenizer.save_pretrained(save_dir) if args.max_steps > 0 and global_step >= args.max_steps: From 4669194fa41721d5a47f52d9d018e2ba3105031b Mon Sep 17 00:00:00 2001 From: milen <1649759610@qq.com> Date: Mon, 11 Jul 2022 11:33:11 +0000 Subject: [PATCH 007/116] initial commit --- examples/text_matching/diffcse/README.md | 131 +++---- .../text_matching/diffcse/custom_ernie.py | 3 +- examples/text_matching/diffcse/data.py | 74 +--- .../text_matching/diffcse/eval_metrics.py | 88 ++--- examples/text_matching/diffcse/model.py | 366 +++++++++--------- examples/text_matching/diffcse/run_diffcse.py | 323 ++++++++-------- examples/text_matching/diffcse/run_eval.sh | 20 +- examples/text_matching/diffcse/run_infer.sh | 20 +- examples/text_matching/diffcse/run_train.sh | 31 +- examples/text_matching/diffcse/test_train.sh | 25 -- examples/text_matching/diffcse/utils.py | 3 +- 11 files changed, 501 insertions(+), 583 deletions(-) delete mode 100644 examples/text_matching/diffcse/test_train.sh diff --git a/examples/text_matching/diffcse/README.md b/examples/text_matching/diffcse/README.md index b13a5662d813..f476101ccb2b 100644 --- a/examples/text_matching/diffcse/README.md +++ b/examples/text_matching/diffcse/README.md @@ -24,48 +24,49 @@ DiffCSE/ 可以运行如下命令，开始模型训练并且进行模型测试。 ```shell -export CUDA_VISIBLE_DEVICES=0,1,2,3 - -python -u -m paddle.distributed.launch --gpus ${gpu_ids} \ - run_diffcse.py \ - --mode "train" \ - --extractor_name "rocketqa-zh-dureader-query-encoder" \ - --generator_name "ernie-1.0" \ - --discriminator_name "ernie-1.0" \ - --max_seq_length "128" \ - --output_emb_size "32" \ - --train_set_file ./data/train.txt \ - --eval_set_file ./data/test_v1.txt \ - --save_dir ./checkpoints \ - --save_steps "10000" \ - --eval_steps "1000" \ - --batch_size "32" \ - --epochs "3" \ - --learning_rate "3e-5" \ - --weight_decay "0.01" \ - --warmup_proportion "0.01" \ - --dropout "0.1" \ - --margin "0.0" \ - --scale "20" \ - --seed "0" \ - --device "gpu" +gpu_ids=0 +export CUDA_VISIBLE_DEVICES=${gpu_ids} +log_dir="log_diffcse_ErG3D3" + +python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ + run_diffcse.py \ + --mode "train" \ + --encoder_name "rocketqa-zh-dureader-query-encoder" \ + --generator_name "ernie-3.0-base-zh" \ + --discriminator_name "ernie-3.0-base-zh" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --train_set_file "../dataset/100w/train_webdata.txt" \ + --eval_set_file "../dataset/100w/test_v1.txt" \ + --save_dir "./checkpoints_diffcse_ErG3D3" \ + --log_dir ${log_dir} \ + --save_steps "50000" \ + --eval_steps "1000" \ + --batch_size "32" \ + --epochs "3" \ + --mlm_probability "0.15" \ + --lambda_weight "0.15" \ + --learning_rate "3e-5" \ + --weight_decay "0.01" \ + --warmup_proportion "0.01" \ + --seed "0" \ + --device "gpu" ``` 可支持配置的参数： * `mode`：可选，用于指明本次运行是模型训练、模型评估还是模型预测，仅支持[train, eval, infer]三种模式；默认为 infer。 -* `extractor_name`：可选，DiffCSE模型中用于向量抽取的模型名称；默认为 ernie-1.0。 +* `encoder_name`：可选，DiffCSE模型中用于向量抽取的模型名称；默认为 ernie-3.0-base-zh。 * `generator_name`: 可选，DiffCSE模型中生成器的模型名称；默认为 ernie-3.0-base-zh。 * `discriminator_name`: 可选，DiffCSE模型中判别器的模型名称；默认为 rocketqa-zh-dureader-query-encoder。 * `epochs`: 模型训练轮次，默认为3。 * `batch_size`：可选，批处理大小，请结合显存情况进行调整，若出现显存不足，请适当调低这一参数；默认为32。 * `learning_rate`：可选，Fine-tune 的最大学习率；默认为5e-5。 * `weight_decay`：可选，控制正则项力度的参数，用于防止过拟合，默认为0.01。 -* `warmup_proption`：可选，学习率 warmup 策略的比例，如果0.1，则学习率会在前10%训练step的过程中从0慢慢增长到 learning_rate, 而后再缓慢衰减，默认为0.01。 +* `warmup_proportion`：可选，学习率 warmup 策略的比例，如果0.1，则学习率会在前10%训练step的过程中从0慢慢增长到 learning_rate, 而后再缓慢衰减，默认为0.01。 +* `mlm_probability`：可选，利用生成器预测时，控制单词掩码的比例，默认为0.15。 +* `lambda_weight`：可选，控制RTD任务loss的占比，默认为0.15。 * `max_seq_length`：可选，ERNIE-Gram 模型使用的最大序列长度，最大不能超过512, 若出现显存不足，请适当调低这一参数；默认为128。 * `output_emb_size`：可选，向量抽取模型输出向量的维度；默认为32。 -* `dropout`：可选，DiffCSE 网络前向使用的 dropout 取值；默认 0.1。 -* `margin`：可选，用于计算损失时，保持正例相对于负例的 margin 值；默认 0.0. -* `scale`：可选，在计算 cross_entropy loss 之前对 cosine 相似度进行缩放的因子；默认为 20。 * `train_set_file`：可选，用于指定训练集的路径。 * `eval_set_file`：可选，用于指定验证集的路径。 * `save_dir`：可选，保存训练模型的目录； @@ -78,7 +79,7 @@ python -u -m paddle.distributed.launch --gpus ${gpu_ids} \ 如： ```text checkpoints/ -├── model_10000 +├── model_best_spearman │   ├── model_state.pdparams │   ├── tokenizer_config.json │   ├── special_tokens_map.json @@ -90,24 +91,22 @@ checkpoints/ 可以运行如下命令，进行模型评估。 ```shell -export CUDA_VISIBLE_DEVICES=0 - -python run_diffcse.py \ - --mode "eval" \ - --extractor_name "rocketqa-zh-dureader-query-encoder" \ - --generator_name "ernie-1.0" \ - --discriminator_name "ernie-1.0" \ - --max_seq_length "128" \ - --output_emb_size "32" \ - --eval_set_file ./data/test_v1.txt \ - --ckpt_dir ./checkpoints/checkpoint_10000 \ - --batch_size "8" \ - --dropout "0.1" \ - --margin "0.0" \ - --scale "20" \ - --dup_rate "0.0" \ - --seed "0" \ - --device "gpu" +gpu_ids=0 +export CUDA_VISIBLE_DEVICES=${gpu_ids} + +log_dir="log_eval" +python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ + run_diffcse.py \ + --mode "eval" \ + --encoder_name "rocketqa-zh-dureader-query-encoder" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --eval_set_file "../diffcse/data/test_v1.txt" \ + --ckpt_dir "./checkpoints_diffcse_ErG3D3/best_spearman" \ + --batch_size "8" \ + --seed "0" \ + --device "gpu" + ``` 可支持配置的参数： * `ckpt_dir`: 用于指定进行模型评估的checkpoint路径。 @@ -118,25 +117,23 @@ python run_diffcse.py \ 可以运行如下命令，进行模型预测： ```shell -export CUDA_VISIBLE_DEVICES=0 - -python run_diffcse.py \ - --mode "eval" \ - --extractor_name "rocketqa-zh-dureader-query-encoder" \ - --generator_name "ernie-1.0" \ - --discriminator_name "ernie-3.0-base-zh" \ - --max_seq_length "128" \ - --output_emb_size "32" \ - --infer_set_file "./data/test.txt" \ - --ckpt_dir "./checkpoints/best_spearman" \ +gpu_ids=0 +export CUDA_VISIBLE_DEVICES=${gpu_ids} + +log_dir="log_infer" +python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ + run_diffcse.py \ + --mode "infer" \ + --encoder_name "rocketqa-zh-dureader-query-encoder" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --infer_set_file "../diffcse/data/test.txt" \ + --ckpt_dir "./checkpoints_diffcse_ErG3D3/best_spearman" \ --save_infer_path "./infer_result.txt" \ - --batch_size "8" \ - --dropout "0.1" \ - --margin "0.0" \ - --scale "20" \ - --dup_rate "0.0" \ - --seed "0" \ - --device "gpu" + --batch_size "8" \ + --seed "0" \ + --device "gpu" + ``` diff --git a/examples/text_matching/diffcse/custom_ernie.py b/examples/text_matching/diffcse/custom_ernie.py index d31e67ab523c..0c0c910feb38 100644 --- a/examples/text_matching/diffcse/custom_ernie.py +++ b/examples/text_matching/diffcse/custom_ernie.py @@ -648,8 +648,7 @@ def forward(self, task_type_ids=task_type_ids) if cls_input is not None: - embedding_output = paddle.concat( - [cls_input.unsqueeze(1), embedding_output[:, 1:, :]], axis=1) + embedding_output = paddle.concat([cls_input.unsqueeze(1), embedding_output[:, 1:, :]], axis=1) encoder_outputs = self.encoder(embedding_output, attention_mask) sequence_output = encoder_outputs diff --git a/examples/text_matching/diffcse/data.py b/examples/text_matching/diffcse/data.py index b2535262e99d..96ffafdb6e8a 100644 --- a/examples/text_matching/diffcse/data.py +++ b/examples/text_matching/diffcse/data.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. + import paddle import os @@ -22,39 +23,25 @@ def get_special_tokens(): return ["[PAD]", "[CLS]", "[SEP]", "[MASK]", "[UNK]"] - def get_special_token_ids(tokenizer): special_tokens = ["[PAD]", "[CLS]", "[SEP]", "[MASK]", "[UNK]"] return tokenizer.convert_tokens_to_ids(special_tokens) - def get_special_token_dict(tokenizer): special_tokens = ["[PAD]", "[CLS]", "[SEP]", "[MASK]", "[UNK]"] - special_token_dict = dict( - zip(special_tokens, tokenizer.convert_tokens_to_ids(special_tokens))) + special_token_dict = dict(zip(special_tokens, tokenizer.convert_tokens_to_ids(special_tokens))) return special_token_dict -def create_dataloader(dataset, - mode="train", - batch_size=1, - batchify_fn=None, - trans_fn=None): +def create_dataloader(dataset, mode="train", batch_size=1, batchify_fn=None, trans_fn=None): if trans_fn: dataset = dataset.map(trans_fn) shuffle = True if mode == "train" else False if mode == "train": - batch_sampler = paddle.io.DistributedBatchSampler(dataset, - batch_size=batch_size, - shuffle=shuffle) + batch_sampler = paddle.io.DistributedBatchSampler(dataset, batch_size=batch_size, shuffle=shuffle) else: - batch_sampler = paddle.io.BatchSampler(dataset, - batch_size=batch_size, - shuffle=shuffle) - return paddle.io.DataLoader(dataset=dataset, - batch_sampler=batch_sampler, - collate_fn=batchify_fn, - return_list=True) + batch_sampler = paddle.io.BatchSampler(dataset, batch_size=batch_size, shuffle=shuffle) + return paddle.io.DataLoader(dataset=dataset, batch_sampler=batch_sampler, collate_fn=batchify_fn, return_list=True) def convert_example(example, tokenizer, max_seq_length=512, do_evalute=False): @@ -65,10 +52,7 @@ def convert_example(example, tokenizer, max_seq_length=512, do_evalute=False): result += [example["label"]] else: # do_train - encoded_inputs = tokenizer(text=text, - max_seq_len=max_seq_length, - return_attention_mask=True) - # print(encoded_inputs) + encoded_inputs = tokenizer(text=text, max_seq_len=max_seq_length, return_attention_mask=True) input_ids = encoded_inputs["input_ids"] token_type_ids = encoded_inputs["token_type_ids"] attention_mask = encoded_inputs["attention_mask"] @@ -82,12 +66,10 @@ def read_text_single(data_path): data = line.rstrip() yield {"text_a": data, "text_b": data} - def masked_fill(x, mask, value): y = paddle.full(x.shape, value, x.dtype) return paddle.where(mask, y, x) - def mask_tokens(batch_inputs, tokenizer, mlm_probability=0.15): """ Description: Mask input_ids for masked language modeling: 80% MASK, 10% random, 10% original @@ -95,43 +77,28 @@ def mask_tokens(batch_inputs, tokenizer, mlm_probability=0.15): mlm_inputs = batch_inputs.clone() mlm_labels = batch_inputs.clone() - # print(mlm_inputs) probability_matrix = paddle.full(mlm_inputs.shape, mlm_probability) - # print(probability_matrix) - - special_tokens_mask = paddle.cast(paddle.zeros(mlm_inputs.shape), - dtype=bool) + + special_tokens_mask = paddle.cast(paddle.zeros(mlm_inputs.shape), dtype=bool) for special_token_id in get_special_token_ids(tokenizer): - special_tokens_mask |= (mlm_inputs == special_token_id) + special_tokens_mask |= (mlm_inputs==special_token_id) - probability_matrix = masked_fill(probability_matrix, special_tokens_mask, - 0.0) - # print(probability_matrix) + + probability_matrix = masked_fill(probability_matrix, special_tokens_mask, 0.0) - masked_indices = paddle.cast(paddle.bernoulli(probability_matrix), - dtype=bool) + masked_indices = paddle.cast(paddle.bernoulli(probability_matrix), dtype=bool) mlm_labels = masked_fill(mlm_labels, ~masked_indices, -100) # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) - indices_replaced = paddle.cast(paddle.bernoulli( - paddle.full(mlm_inputs.shape, 0.8)), - dtype=bool) & masked_indices - mlm_inputs = masked_fill(mlm_inputs, indices_replaced, - tokenizer.mask_token_id) + indices_replaced = paddle.cast(paddle.bernoulli(paddle.full(mlm_inputs.shape, 0.8)), dtype=bool) & masked_indices + mlm_inputs = masked_fill(mlm_inputs, indices_replaced, tokenizer.mask_token_id) # 10% of the time, we replace masked input tokens with random word - indices_random = paddle.cast( - paddle.bernoulli(paddle.full(mlm_inputs.shape, 0.5)), - dtype=bool) & masked_indices & ~indices_replaced - random_words = paddle.randint(0, - len(tokenizer), - mlm_inputs.shape, - dtype=mlm_inputs.dtype) + indices_random = paddle.cast(paddle.bernoulli(paddle.full(mlm_inputs.shape, 0.5)) ,dtype=bool) & masked_indices & ~indices_replaced + random_words = paddle.randint(0, len(tokenizer), mlm_inputs.shape, dtype=mlm_inputs.dtype) mlm_inputs = paddle.where(indices_random, random_words, mlm_inputs) # The rest of the time (10% of the time) we keep the masked input tokens unchanged - # print(mlm_inputs) - # print(mlm_labels) return mlm_inputs, mlm_labels @@ -166,8 +133,7 @@ def word_repetition(input_ids, token_type_ids, dup_rate=0.32): if (actual_len > 5): dup_len = random.randint(a=0, b=max(2, int(dup_rate * actual_len))) # Skip cls and sep position - dup_word_index = random.sample(list(range(1, actual_len - 1)), - k=dup_len) + dup_word_index = random.sample(list(range(1, actual_len - 1)), k=dup_len) r_input_id = [] r_token_type_id = [] @@ -190,5 +156,5 @@ def word_repetition(input_ids, token_type_ids, dup_rate=0.32): repetitied_input_ids[batch_id] += [0] * pad_len repetitied_token_type_ids[batch_id] += [0] * pad_len - return paddle.to_tensor(repetitied_input_ids), paddle.to_tensor( - repetitied_token_type_ids) + return paddle.to_tensor(repetitied_input_ids), paddle.to_tensor(repetitied_token_type_ids) + diff --git a/examples/text_matching/diffcse/eval_metrics.py b/examples/text_matching/diffcse/eval_metrics.py index bb102363109e..c6b3ea9225d4 100644 --- a/examples/text_matching/diffcse/eval_metrics.py +++ b/examples/text_matching/diffcse/eval_metrics.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. + import numpy as np from scipy import stats from sklearn import metrics @@ -20,26 +21,20 @@ def eval_metrics(labels, sims): eval_res = {} - spearman_corr = stats.spearmanr(labels, sims).correlation eval_res["spearman_corr"] = spearman_corr print("Spearman corr --> {:.5f}\n".format(spearman_corr)) - labels_2_cls = np.where(np.greater_equal(labels, 3), np.ones_like(labels), - np.zeros_like(labels)) + labels_2_cls = np.where(np.greater_equal(labels, 3), np.ones_like(labels), np.zeros_like(labels)) - pre, rec, thr = metrics.precision_recall_curve(labels_2_cls, - sims, - pos_label=1) + pre, rec, thr = metrics.precision_recall_curve(labels_2_cls, sims, pos_label=1) pre_rec = np.sum([pre, rec], axis=0) best_pre_rec = np.max(pre_rec) best_pre = pre[np.argmax(pre_rec)] best_rec = rec[np.argmax(pre_rec)] best_thr = thr[np.argmax(pre_rec)] eval_res["best_pre_rec_thr"] = [best_pre_rec, best_pre, best_rec, best_thr] - print( - "2-CLS best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}" - .format(best_pre_rec, best_pre, best_rec, best_thr)) + print("2-CLS best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}".format(best_pre_rec, best_pre, best_rec, best_thr)) pre_rec_thr = list(zip(pre, rec, thr)) pre_rec_thr.sort(key=lambda x: x[1], reverse=True) @@ -50,22 +45,20 @@ def eval_metrics(labels, sims): pre_rec_thr_at_K = x break eval_res["best_pre_rec_thr_at_K"] = pre_rec_thr_at_K - print( - "2-CLS best pre@{:.2f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}". - format(at_K, pre_rec_thr_at_K[0], pre_rec_thr_at_K[1], - pre_rec_thr_at_K[2])) + print("2-CLS best pre@{:.2f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}".format(at_K, pre_rec_thr_at_K[0], pre_rec_thr_at_K[1], pre_rec_thr_at_K[2])) - preds_2_cls = np.where(np.greater_equal(sims, best_thr), np.ones_like(sims), - np.zeros_like(sims)) + preds_2_cls = np.where(np.greater_equal(sims, best_thr), np.ones_like(sims), np.zeros_like(sims)) acc_2_cls = metrics.accuracy_score(labels_2_cls, preds_2_cls) auc_2_cls = metrics.roc_auc_score(labels_2_cls, sims) - report_2_cls = metrics.classification_report(labels_2_cls, - preds_2_cls, - labels=[0, 1], - target_names=["0", "1"], - digits=5, - output_dict=True) + report_2_cls = metrics.classification_report( + labels_2_cls, + preds_2_cls, + labels=[0, 1], + target_names=["0", "1"], + digits=5, + output_dict=True + ) eval_res["acc_2_cls"] = acc_2_cls eval_res["auc_2_cls"] = auc_2_cls eval_res["report_2_cls"] = report_2_cls @@ -73,32 +66,21 @@ def eval_metrics(labels, sims): print("2-CLS accuracy --> {}".format(acc_2_cls)) print("2-CLS roc-auc --> {}".format(auc_2_cls)) print("\t".join(["label", "precision", "recall", "f1-score", "support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( - "2-CLS 0", report_2_cls["0"]["precision"], report_2_cls["0"]["recall"], - report_2_cls["0"]["f1-score"], report_2_cls["0"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}\n".format( - "2-CLS 1", report_2_cls["1"]["precision"], report_2_cls["1"]["recall"], - report_2_cls["1"]["f1-score"], report_2_cls["1"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("2-CLS 0", report_2_cls["0"]["precision"], report_2_cls["0"]["recall"], report_2_cls["0"]["f1-score"], report_2_cls["0"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}\n".format("2-CLS 1", report_2_cls["1"]["precision"], report_2_cls["1"]["recall"], report_2_cls["1"]["f1-score"], report_2_cls["1"]["support"])) best_thr_6_cls = [] for pivot in [5, 4, 3, 2, 1]: - labels_pivot = np.where(np.greater_equal(labels, pivot), - np.ones_like(labels), np.zeros_like(labels)) - pre_pivot, rec_pivot, thr_pivot = metrics.precision_recall_curve( - labels_pivot, sims, pos_label=1) + labels_pivot = np.where(np.greater_equal(labels, pivot), np.ones_like(labels), np.zeros_like(labels)) + pre_pivot, rec_pivot, thr_pivot = metrics.precision_recall_curve(labels_pivot, sims, pos_label=1) pre_rec_pivot = np.sum([pre_pivot, rec_pivot], axis=0) best_pre_rec_pivot = np.max(pre_rec_pivot) best_pre_pivot = pre_pivot[np.argmax(pre_rec_pivot)] best_rec_pivot = rec_pivot[np.argmax(pre_rec_pivot)] best_thr_pivot = thr_pivot[np.argmax(pre_rec_pivot)] best_thr_6_cls.append(best_thr_pivot) - eval_res["best_pre_rec_thr_6_cls_at_{}_pivot".format(pivot)] = [ - best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, best_thr_pivot - ] - print( - "6-CLS [split at {} pivot] best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}" - .format(pivot, best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, - best_thr_pivot)) + eval_res["best_pre_rec_thr_6_cls_at_{}_pivot".format(pivot)] = [best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, best_thr_pivot] + print("6-CLS [split at {} pivot] best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}".format(pivot, best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, best_thr_pivot)) preds = [] for s in sims: @@ -116,31 +98,19 @@ def eval_metrics(labels, sims): labels=[0, 1, 2, 3, 4, 5], target_names=["0", "1", "2", "3", "4", "5"], digits=5, - output_dict=True) + output_dict=True + ) eval_res["acc_6_cls"] = acc_6_cls eval_res["report_6_cls"] = report_6_cls - print("6-CLS metrics are evaluated on the best thr --> {}".format( - best_thr_6_cls)) + print("6-CLS metrics are evaluated on the best thr --> {}".format(best_thr_6_cls)) print("6-CLS accuracy --> {}".format(acc_6_cls)) print("\t".join(["label", "precision", "recall", "f1-score", "support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( - "6-CLS 0", report_6_cls["0"]["precision"], report_6_cls["0"]["recall"], - report_6_cls["0"]["f1-score"], report_6_cls["0"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( - "6-CLS 1", report_6_cls["1"]["precision"], report_6_cls["1"]["recall"], - report_6_cls["1"]["f1-score"], report_6_cls["1"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( - "6-CLS 2", report_6_cls["2"]["precision"], report_6_cls["2"]["recall"], - report_6_cls["2"]["f1-score"], report_6_cls["2"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( - "6-CLS 3", report_6_cls["3"]["precision"], report_6_cls["3"]["recall"], - report_6_cls["3"]["f1-score"], report_6_cls["3"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( - "6-CLS 4", report_6_cls["4"]["precision"], report_6_cls["4"]["recall"], - report_6_cls["4"]["f1-score"], report_6_cls["4"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( - "6-CLS 5", report_6_cls["5"]["precision"], report_6_cls["5"]["recall"], - report_6_cls["5"]["f1-score"], report_6_cls["5"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 0", report_6_cls["0"]["precision"], report_6_cls["0"]["recall"], report_6_cls["0"]["f1-score"], report_6_cls["0"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 1", report_6_cls["1"]["precision"], report_6_cls["1"]["recall"], report_6_cls["1"]["f1-score"], report_6_cls["1"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 2", report_6_cls["2"]["precision"], report_6_cls["2"]["recall"], report_6_cls["2"]["f1-score"], report_6_cls["2"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 3", report_6_cls["3"]["precision"], report_6_cls["3"]["recall"], report_6_cls["3"]["f1-score"], report_6_cls["3"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 4", report_6_cls["4"]["precision"], report_6_cls["4"]["recall"], report_6_cls["4"]["f1-score"], report_6_cls["4"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 5", report_6_cls["5"]["precision"], report_6_cls["5"]["recall"], report_6_cls["5"]["f1-score"], report_6_cls["5"]["support"])) return eval_res diff --git a/examples/text_matching/diffcse/model.py b/examples/text_matching/diffcse/model.py index 320db25cbbad..fc7366901cc5 100644 --- a/examples/text_matching/diffcse/model.py +++ b/examples/text_matching/diffcse/model.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. + import numpy as np import paddle @@ -22,243 +23,256 @@ from data import mask_tokens from custom_ernie import ErnieModel as CustomErnie - -class Extractor(nn.Layer): - - def __init__(self, - pretrained_model_name, - dropout=None, - margin=0.0, - scale=20, - output_emb_size=None): - super().__init__() +class ProjectionMLP(nn.Layer): + def __init__(self, in_dim): + super(ProjectionMLP, self).__init__() + hidden_dim = in_dim * 2 + out_dim = in_dim + affine=False + list_layers = [nn.Linear(in_dim, hidden_dim, bias_attr=False), + nn.BatchNorm1D(hidden_dim), + nn.ReLU()] + list_layers += [nn.Linear(hidden_dim, out_dim, bias_attr=False), + nn.BatchNorm1D(out_dim)] + self.net = nn.Sequential(*list_layers) + + def forward(self, x): + return self.net(x) + +class Similarity(nn.Layer): + """ + Dot product or cosine similarity + """ + + def __init__(self, temp): + super(Similarity, self).__init__() + self.temp = temp + self.cos = nn.CosineSimilarity(axis=-1) + self.record = None + self.pos_avg = 0.0 + self.neg_avg = 0.0 + + def forward(self, x, y, one_vs_one=False): + if one_vs_one: + sim = self.cos(x, y) + return sim + + x=x.unsqueeze(1) + y=y.unsqueeze(0) + sim = self.cos(x, y) + self.record = sim.detach() + min_size = min(self.record.shape[0], self.record.shape[1]) + num_item = self.record.shape[0] * self.record.shape[1] + self.pos_avg = paddle.diag(self.record).sum().item() / min_size + self.neg_avg = (self.record.sum().item() - paddle.diag(self.record).sum().item()) / (num_item-min_size) + return sim / self.temp + + +class Encoder(nn.Layer): + def __init__(self, pretrained_model_name, temp=0.05, output_emb_size=None): + super(Encoder, self).__init__() self.ptm = AutoModel.from_pretrained(pretrained_model_name) - self.dropout = nn.Dropout(dropout if dropout is not None else 0.1) # if output_emb_size is greater than 0, then add Linear layer to reduce embedding_size self.output_emb_size = output_emb_size - if output_emb_size > 0: - weight_attr = paddle.ParamAttr( - initializer=paddle.nn.initializer.TruncatedNormal(std=0.02)) - self.emb_reduce_linear = paddle.nn.Linear(768, - output_emb_size, - weight_attr=weight_attr) - - self.margin = margin - # Used scaling cosine similarity to ease converge - self.scale = scale - - def get_pooled_embedding(self, - input_ids, - token_type_ids=None, - position_ids=None, - attention_mask=None, - with_pooler=False): + self.mlp = ProjectionMLP(self.ptm.config['hidden_size']) + + if output_emb_size is not None: + self.emb_reduce_linear = nn.Linear(self.ptm.config['hidden_size'], output_emb_size) + + self.temp = temp + self.sim = Similarity(temp) + + def get_pooled_embedding(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, with_pooler=False): # Note: cls_embedding is poolerd embedding with act tanh - sequence_output, cls_embedding = self.ptm(input_ids, token_type_ids, - position_ids, attention_mask) + sequence_output, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids, attention_mask) if not with_pooler: ori_cls_embedding = sequence_output[:, 0, :] - if self.output_emb_size > 0: - cls_embedding = self.emb_reduce_linear(ori_cls_embedding) - - cls_embedding = self.dropout(cls_embedding) - cls_embedding = F.normalize(cls_embedding, p=2, axis=-1) - return cls_embedding, ori_cls_embedding - - def cosine_sim(self, - query_input_ids, - key_input_ids, - query_token_type_ids=None, - query_position_ids=None, - query_attention_mask=None, - key_token_type_ids=None, - key_position_ids=None, - key_attention_mask=None, - with_pooler=False): + else: + ori_cls_embedding = cls_embedding + + mlp_cls_embedding = self.mlp(ori_cls_embedding) + if self.output_emb_size is not None: + cls_embedding = self.emb_reduce_linear(mlp_cls_embedding) + + return cls_embedding, mlp_cls_embedding + + def cosine_sim( + self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + query_position_ids=None, + query_attention_mask=None, + key_token_type_ids=None, + key_position_ids=None, + key_attention_mask=None, + with_pooler=False + ): query_cls_embedding, _ = self.get_pooled_embedding( query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask, - with_pooler=with_pooler) + with_pooler=with_pooler + ) key_cls_embedding, _ = self.get_pooled_embedding( key_input_ids, key_token_type_ids, key_position_ids, key_attention_mask, - with_pooler=with_pooler) + with_pooler=with_pooler + ) - cosine_sim = paddle.sum(query_cls_embedding * key_cls_embedding, - axis=-1) + cosine_sim = self.sim(query_cls_embedding, key_cls_embedding, one_vs_one=True) + # cosine_sim = paddle.sum(query_cls_embedding * key_cls_embedding, axis=-1) return cosine_sim - def forward(self, - query_input_ids, - key_input_ids, - query_token_type_ids=None, - query_position_ids=None, - query_attention_mask=None, - key_token_type_ids=None, - key_position_ids=None, - key_attention_mask=None, - with_pooler=False): - query_cls_embedding, ori_query_cls_embedding = self.get_pooled_embedding( + def forward( + self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + query_position_ids=None, + query_attention_mask=None, + key_token_type_ids=None, + key_position_ids=None, + key_attention_mask=None, + with_pooler=False + ): + query_cls_embedding, mlp_query_cls_embedding = self.get_pooled_embedding( query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask, - with_pooler=with_pooler) - key_cls_embedding, ori_key_cls_embedding = self.get_pooled_embedding( + with_pooler=with_pooler + ) + key_cls_embedding, mlp_key_cls_embedding = self.get_pooled_embedding( key_input_ids, key_token_type_ids, key_position_ids, key_attention_mask, - with_pooler=with_pooler) - cosine_sim = paddle.matmul(query_cls_embedding, - key_cls_embedding, - transpose_y=True) - - # substract margin from all positive samples cosine_sim() - margin_diag = paddle.full(shape=[query_cls_embedding.shape[0]], - fill_value=self.margin, - dtype=paddle.get_default_dtype()) - - cosine_sim = cosine_sim - paddle.diag(margin_diag) + with_pooler=with_pooler + ) - # scale cosine to ease training converge - cosine_sim *= self.scale + cosine_sim = self.sim(query_cls_embedding, key_cls_embedding) labels = paddle.arange(0, query_cls_embedding.shape[0], dtype="int64") labels = paddle.reshape(labels, shape=[-1, 1]) - loss = F.cross_entropy(input=cosine_sim, label=labels) - ori_cls_embedding = paddle.concat( - [ori_query_cls_embedding, ori_key_cls_embedding], axis=0) - return loss, ori_cls_embedding + mlp_cls_embedding = paddle.concat([mlp_query_cls_embedding, mlp_key_cls_embedding], axis=0) + return loss, mlp_cls_embedding class Discriminator(nn.Layer): - def __init__(self, ptm_model_name): super(Discriminator, self).__init__() self.ptm = CustomErnie.from_pretrained(ptm_model_name) self.classifier = nn.Linear(self.ptm.config["hidden_size"], 2) - - def forward(self, - input_ids, - labels, - cls_input, - token_type_ids=None, - attention_mask=None): - sequence_output, _ = self.ptm(input_ids, - token_type_ids=token_type_ids, - attention_mask=attention_mask, - cls_input=cls_input) + + def forward(self, input_ids, labels, cls_input, token_type_ids=None, attention_mask=None): + sequence_output, _ = self.ptm(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, cls_input=cls_input) pred_scores = self.classifier(sequence_output) loss = F.cross_entropy(input=pred_scores, label=labels) - return loss + return loss, pred_scores.argmax(-1) class DiffCSE(nn.Layer): - - def __init__(self, args, tokenizer): + def __init__(self, encoder_name, generator_name, discriminator_name, gen_tokenizer, dis_tokenizer, temp=0.05, output_emb_size=32, mlm_probability=0.15, lambda_weight=0.15): super(DiffCSE, self).__init__() - self.args = args - self.mode = args.mode - self.tokenizer = tokenizer - self.generator_name = args.generator_name - self.discriminator_name = args.discriminator_name - - self.extractor = Extractor(args.extractor_name, - margin=args.margin, - scale=args.scale, - output_emb_size=args.output_emb_size) - self.generator = ErnieForMaskedLM.from_pretrained(args.generator_name) - self.discriminator = Discriminator(args.discriminator_name) - - def train_forward(self, - query_input_ids, - key_input_ids, - query_token_type_ids=None, - key_token_type_ids=None, - query_attention_mask=None, - key_attention_mask=None, - cls_token=1): - - # extract senmantic vector with extractor and then comput CL loss - loss, ori_cls_embedding = self.extractor( - query_input_ids, - key_input_ids, - query_token_type_ids=query_token_type_ids, - key_token_type_ids=key_token_type_ids, - query_attention_mask=query_attention_mask, - key_attention_mask=key_attention_mask) + self.encoder_name = encoder_name + self.generator_name = generator_name + self.discriminator_name = discriminator_name + self.gen_tokenizer = gen_tokenizer + self.dis_tokenizer = dis_tokenizer + self.temp = temp + self.output_emb_size = output_emb_size + self.mlm_probability = mlm_probability + self.lambda_weight = lambda_weight + + self.encoder = Encoder(encoder_name, temp=temp, output_emb_size=output_emb_size) + self.generator = ErnieForMaskedLM.from_pretrained(generator_name) + self.discriminator = Discriminator(discriminator_name) + + self.rtd_acc = 0.0 + self.rtd_rep_acc = 0.0 + self.rtd_fix_acc = 0.0 + + def train_forward(self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + key_token_type_ids=None, + query_attention_mask=None, + key_attention_mask=None + ): + + # extract senmantic vector with encoder and then comput CL loss + loss, mlp_cls_embedding = self.encoder(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask) + with paddle.no_grad(): # mask tokens for query and key input_ids and then predict mask token with generator input_ids = paddle.concat([query_input_ids, key_input_ids], axis=0) - attention_mask = paddle.concat( - [query_attention_mask, key_attention_mask], axis=0) - mlm_input_ids, _ = mask_tokens( - paddle.concat([query_input_ids, key_input_ids], axis=0), - self.tokenizer, - mlm_probability=self.args.mlm_probability) - pred_tokens = self.generator( - mlm_input_ids, attention_mask=attention_mask).argmax(-1) - - pred_tokens[:, 0] = cls_token - e_inputs = pred_tokens * attention_mask - replaced = pred_tokens != input_ids - e_labels = paddle.cast(replaced, dtype="int64") * attention_mask + attention_mask = paddle.concat([query_attention_mask, key_attention_mask], axis=0) + mlm_input_ids, _ = mask_tokens(paddle.concat([query_input_ids, key_input_ids], axis=0), self.gen_tokenizer, mlm_probability=self.mlm_probability) + # predict tokens using generator + pred_tokens = self.generator(mlm_input_ids, attention_mask=attention_mask).argmax(axis=-1) - rtd_loss = self.discriminator(e_inputs, - e_labels, - cls_input=ori_cls_embedding) - loss = loss + self.args.lambda_weight * rtd_loss + pred_tokens = pred_tokens.detach() - return loss + if self.generator_name != self.discriminator_name: + pred_tokens = self.encode_by_discriminator(pred_tokens) + input_ids = self.encode_by_discriminator(input_ids) - def test_forward(self, - query_input_ids, - key_input_ids, - query_token_type_ids=None, - key_token_type_ids=None, - query_attention_mask=None, - key_attention_mask=None): + pred_tokens[:, 0] = self.dis_tokenizer.cls_token_id + e_inputs = pred_tokens * attention_mask + replaced = pred_tokens != input_ids + e_labels = paddle.cast(replaced, dtype="int64") * attention_mask + rtd_loss, prediction = self.discriminator(e_inputs, e_labels, cls_input=mlp_cls_embedding) + loss = loss + self.lambda_weight * rtd_loss + + rep = (e_labels == 1) * attention_mask + fix = (e_labels == 0) * attention_mask + self.rtd_rep_acc = float((prediction * rep).sum() / rep.sum()) + self.rtd_fix_acc = float(1.0 - (prediction * fix).sum() / fix.sum()) + self.rtd_acc = float(((prediction == e_labels) * attention_mask).sum()/attention_mask.sum()) + + return loss, rtd_loss + + def encode_by_discriminator(self, batch_tokens): + new_tokens = [] + for one_tokens in batch_tokens: + one_gen_tokens = self.gen_tokenizer.convert_ids_to_tokens(one_tokens.tolist()) + new_tokens.append(self.dis_tokenizer.convert_tokens_to_ids(one_gen_tokens)) + + return paddle.to_tensor(new_tokens) + + + def test_forward(self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + key_token_type_ids=None, + query_attention_mask=None, + key_attention_mask=None + ): # compute cosine similarity for query and key text - cos_sim = self.extractor.cosine_sim( - query_input_ids, - key_input_ids, - query_token_type_ids=query_token_type_ids, - key_token_type_ids=key_token_type_ids, - query_attention_mask=query_attention_mask, - key_attention_mask=key_attention_mask) + cos_sim = self.encoder.cosine_sim(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask) return cos_sim - def forward(self, - query_input_ids, + def forward(self, + query_input_ids, key_input_ids, - query_token_type_ids=None, + query_token_type_ids=None, key_token_type_ids=None, query_attention_mask=None, key_attention_mask=None, - cls_token=1, - mode="train"): + mode="train" + ): if mode == "train": - return self.train_forward(query_input_ids, - key_input_ids, - query_token_type_ids=query_token_type_ids, - key_token_type_ids=key_token_type_ids, - query_attention_mask=query_attention_mask, - key_attention_mask=key_attention_mask, - cls_token=cls_token) + return self.train_forward(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask) else: - return self.test_forward(query_input_ids, - key_input_ids, - query_token_type_ids=query_token_type_ids, - key_token_type_ids=key_token_type_ids, - query_attention_mask=query_attention_mask, - key_attention_mask=key_attention_mask) + return self.test_forward(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask) diff --git a/examples/text_matching/diffcse/run_diffcse.py b/examples/text_matching/diffcse/run_diffcse.py index 9e3a47d9c10f..1af2f0686d40 100644 --- a/examples/text_matching/diffcse/run_diffcse.py +++ b/examples/text_matching/diffcse/run_diffcse.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. + import os import time import random @@ -26,16 +27,18 @@ from paddlenlp.data import Stack, Tuple, Pad from paddlenlp.datasets import load_dataset from paddlenlp.transformers import LinearDecayWithWarmup +from visualdl import LogWriter -from model import DiffCSE, Extractor +from model import DiffCSE, Encoder from utils import set_seed from eval_metrics import eval_metrics from data import read_text_single, read_text_pair, convert_example, create_dataloader, word_repetition + # yapf: disable parser = argparse.ArgumentParser() parser.add_argument("--mode", choices=["train", "eval", "infer"], default="infer", help="Select which mode to run model, defaults to infer.") -parser.add_argument("--extractor_name", type=str, help="The extractor model name or path that you wanna train based on.") +parser.add_argument("--encoder_name", type=str, help="The sentence_encoder name or path that you wanna train based on.") parser.add_argument("--generator_name", type=str, help="The generator model name or path that you wanna train based on.") parser.add_argument("--discriminator_name", type=str, help="The discriminator model name or path that you wanna train based on.") parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization.") @@ -43,8 +46,9 @@ parser.add_argument("--train_set_file", type=str, help="The full path of train_set_file.") parser.add_argument("--eval_set_file", type=str, help="The full path of eval_set_file.") parser.add_argument("--infer_set_file", type=str, help="The full path of infer_set_file.") -parser.add_argument("--ckpt_dir", default=None, type=str, help="The ckpt directory where the model checkpoints will be loaded.") -parser.add_argument("--save_dir", default="./checkpoints", type=str, help="The save directory where the model checkpoints will be written.") +parser.add_argument("--ckpt_dir", default=None, type=str, help="The ckpt directory where the model checkpoints will be loaded when doing evalution/inference.") +parser.add_argument("--save_dir", default="./checkpoints", type=str, help="The directory where the model checkpoints will be written.") +parser.add_argument("--log_dir", default=None, type=str, help="The directory where log will be written.") parser.add_argument("--save_infer_path", default="./infer_result.txt", type=str, help="The save directory where the inference result will be written.") parser.add_argument("--save_steps", type=int, default=10000, help="Step interval for saving checkpoint.") parser.add_argument("--eval_steps", type=int, default=10000, help="Step interval for evaluation.") @@ -54,10 +58,7 @@ parser.add_argument("--learning_rate", default=1e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--warmup_proportion", default=0.0, type=float, help="Linear warmup proption over the training process.") -parser.add_argument("--dropout", default=0.1, type=float, help="Dropout for pretrained model encoder.") -parser.add_argument("--margin", default=0.0, type=float, help="Margin beteween pos_sample and neg_samples.") -parser.add_argument("--scale", default=20, type=int, help="Scale for pair-wise margin_rank_loss.") -parser.add_argument("--dup_rate", default=0.32, type=float, help="duplicate rate for word reptition.") +parser.add_argument("--temp", default=0.05, type=float, help="Temperature for softmax.") parser.add_argument("--mlm_probability", default=0.15, type=float, help="The ratio for masked language model.") parser.add_argument("--lambda_weight", default=0.15, type=float, help="The weight for RTD loss.") parser.add_argument("--seed", type=int, default=1000, help="Random seed for initialization.") @@ -67,9 +68,7 @@ def do_infer(model, tokenizer, data_loader): - assert isinstance( - model, - Extractor), "please make sure that model is instance of Extractor." + assert isinstance(model, Encoder), "please make sure that model is instance of Encoder." sims = [] model.eval() with paddle.no_grad(): @@ -82,7 +81,7 @@ def do_infer(model, tokenizer, data_loader): key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask, - ) + ) sims.append(cosine_sim.numpy()) sims = np.concatenate(sims, axis=0) model.train() @@ -90,13 +89,9 @@ def do_infer(model, tokenizer, data_loader): def do_eval(model, tokenizer, data_loader): - assert isinstance( - model, - Extractor), "please make sure that model is instance of Extractor." + assert isinstance(model, Encoder), "please make sure that model is instance of Encoder." total_num = 0 - spearman_corr = 0.0 - sims = [] - labels = [] + sims, labels = [], [] model.eval() with paddle.no_grad(): for batch in data_loader: @@ -109,38 +104,40 @@ def do_eval(model, tokenizer, data_loader): key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask, - ) + ) sims.append(cosine_sim.numpy()) labels.append(label.numpy()) - + sims = np.concatenate(sims, axis=0) - labels = np.concatenate(labels, axis=0) + labels = np.concatenate(labels, axis=0) eval_res = eval_metrics(labels, sims) model.train() return eval_res, total_num -def do_train(model, tokenizer, train_data_loader, dev_data_loader): - num_training_steps = args.max_steps if args.max_steps > 0 else len( - train_data_loader) * args.epochs +def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): + num_training_steps = args.max_steps if args.max_steps > 0 else len(train_data_loader) * args.epochs - lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps, - args.warmup_proportion) + lr_scheduler = LinearDecayWithWarmup( + args.learning_rate, + num_training_steps, + args.warmup_proportion + ) - decay_params = [ - p.name for n, p in model.named_parameters() - if not any(nd in n for nd in ["bias", "norm"]) - ] + decay_params = [p.name for n, p in model.named_parameters() if not any(nd in n for nd in ["bias", "norm"])] optimizer = paddle.optimizer.AdamW( learning_rate=lr_scheduler, parameters=model.parameters(), weight_decay=args.weight_decay, - apply_decay_param_fun=lambda x: x in decay_params) + apply_decay_param_fun=lambda x: x in decay_params + ) global_step = 0 best_spearman = 0.0 best_pre_rec = 0.0 - best_rec95 = 0.0 + best_pre = 0.0 + best_rec = 0.0 + best_rec95 = 0.0 tic_train = time.time() model = paddle.DataParallel(model) model.train() @@ -148,111 +145,120 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader): for step, batch in enumerate(train_data_loader, start=1): query_input_ids, query_token_type_ids, query_attention_mask, key_input_ids, key_token_type_ids, key_attention_mask = batch - if (args.dup_rate > 0): - query_input_ids, query_token_type_ids = word_repetition( - query_input_ids, query_token_type_ids, args.dup_rate) - key_input_ids, key_token_type_ids = word_repetition( - key_input_ids, key_token_type_ids, args.dup_rate) - - loss = model(query_input_ids, - key_input_ids, - query_token_type_ids=query_token_type_ids, - key_token_type_ids=key_token_type_ids, - query_attention_mask=query_attention_mask, - key_attention_mask=key_attention_mask, - cls_token=tokenizer.cls_token_id) + loss, rtd_loss = model( + query_input_ids, + key_input_ids, + query_token_type_ids=query_token_type_ids, + key_token_type_ids=key_token_type_ids, + query_attention_mask=query_attention_mask, + key_attention_mask=key_attention_mask + ) global_step += 1 if global_step % (args.eval_steps // 10) == 0 and rank == 0: - print( - "global step {}, epoch: {}, batch: {}, loss: {:.5f}, speed: {:.2f} step/s" - .format(global_step, epoch, step, loss.item(), - (args.eval_steps // 10) / - (time.time() - tic_train))) + print("global step {}, epoch: {}, batch: {}, loss: {:.5f}, rtd_loss: {:.5f}, rtd_acc: {:.5f}, rtd_rep_acc: {:.5f}, rtd_fix_acc: {:.5f}, speed: {:.2f} step/s".format(global_step, epoch, step, loss.item(), rtd_loss.item(), model._layers.rtd_acc, model._layers.rtd_rep_acc, model._layers.rtd_fix_acc, (args.eval_steps // 10) / (time.time() - tic_train))) + writer.add_scalar(tag="train/loss", step=global_step, value=loss.item()) + writer.add_scalar(tag="train/rtd_loss", step=global_step, value=rtd_loss.item()) + writer.add_scalar(tag="train/rtd_acc", step=global_step, value=model._layers.rtd_acc) + writer.add_scalar(tag="train/rtd_rep_acc", step=global_step, value=model._layers.rtd_rep_acc) + writer.add_scalar(tag="train/rtd_fix_acc", step=global_step, value=model._layers.rtd_fix_acc) + tic_train = time.time() if global_step % args.eval_steps == 0 and rank == 0: - eval_res, total_num = do_eval(model._layers.extractor, - tokenizer, dev_data_loader) + eval_res, total_num = do_eval(model._layers.encoder, tokenizer, dev_data_loader) spearman = eval_res["spearman_corr"] + if best_spearman < spearman: - print( - "best checkpoint has been updated: from last best_spearman {} --> new spearman {}." - .format(best_spearman, spearman)) + print("best checkpoint has been updated: from last best_spearman {} --> new spearman {}.".format(best_spearman, spearman)) best_spearman = spearman # save best model save_dir = os.path.join(args.save_dir, "best_spearman") if not os.path.exists(save_dir): os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, - "model_state.pdparams") - paddle.save(model._layers.extractor.state_dict(), - save_param_path) + save_param_path = os.path.join(save_dir, "model_state.pdparams") + paddle.save(model._layers.encoder.state_dict(), save_param_path) tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), - "w", - encoding="utf-8") as f: - f.write( - str(global_step) + " " + str(epoch) + " " + - str(batch)) - + with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: + f.write(str(global_step)+" "+str(epoch)+ " "+ str(step)) + + writer.add_scalar(tag="eval/spearman", step=global_step, value=spearman) + pre_rec = eval_res["best_pre_rec_thr"][0] if best_pre_rec < pre_rec: - print( - "best checkpoint has been updated: from last best_pre_rec {} --> new pre_rec {}." - .format(best_pre_rec, pre_rec)) + print("best checkpoint has been updated: from last best_pre_rec {} --> new pre_rec {}.".format(best_pre_rec, pre_rec)) best_pre_rec = pre_rec # save best model save_dir = os.path.join(args.save_dir, "best_pre_rec") if not os.path.exists(save_dir): os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, - "model_state.pdparams") - paddle.save(model._layers.extractor.state_dict(), - save_param_path) + save_param_path = os.path.join(save_dir, "model_state.pdparams") + paddle.save(model._layers.encoder.state_dict(), save_param_path) + tokenizer.save_pretrained(save_dir) + with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: + f.write(str(global_step)+" "+str(epoch)+ " "+ str(step)) + + writer.add_scalar(tag="eval/pre_rec", step=global_step, value=pre_rec) + + pre = eval_res["best_pre_rec_thr"][1] + if best_pre < pre: + print("best checkpoint has been updated: from last best_pre {} --> new pre {}.".format(best_pre, pre)) + best_pre = pre + # save best model + save_dir = os.path.join(args.save_dir, "best_pre") + if not os.path.exists(save_dir): + os.makedirs(save_dir) + save_param_path = os.path.join(save_dir, "model_state.pdparams") + paddle.save(model._layers.encoder.state_dict(), save_param_path) tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), - "w", - encoding="utf-8") as f: - f.write( - str(global_step) + " " + str(epoch) + " " + - str(batch)) + with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: + f.write(str(global_step)+" "+str(epoch)+ " "+ str(step)) + + writer.add_scalar(tag="eval/pre", step=global_step, value=pre) + + rec = eval_res["best_pre_rec_thr"][2] + if best_rec < rec: + print("best checkpoint has been updated: from last best_rec {} --> new rec {}.".format(best_rec, rec)) + best_rec = rec + # save best model + save_dir = os.path.join(args.save_dir, "best_rec") + if not os.path.exists(save_dir): + os.makedirs(save_dir) + save_param_path = os.path.join(save_dir, "model_state.pdparams") + paddle.save(model._layers.encoder.state_dict(), save_param_path) + tokenizer.save_pretrained(save_dir) + with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: + f.write(str(global_step)+" "+str(epoch)+ " "+ str(step)) + + writer.add_scalar(tag="eval/rec", step=global_step, value=rec) rec95 = eval_res["best_pre_rec_thr_at_K"][1] if best_rec95 < rec95: - print( - "best checkpoint has been updated: from last best_rec95 {} --> new rec95 {}." - .format(best_rec95, rec95)) + print("best checkpoint has been updated: from last best_rec95 {} --> new rec95 {}.".format(best_rec95, rec95)) best_rec95 = rec95 # save best model save_dir = os.path.join(args.save_dir, "best_rec95") if not os.path.exists(save_dir): os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, - "model_state.pdparams") - paddle.save(model._layers.extractor.state_dict(), - save_param_path) + save_param_path = os.path.join(save_dir, "model_state.pdparams") + paddle.save(model._layers.encoder.state_dict(), save_param_path) tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), - "w", - encoding="utf-8") as f: - f.write( - str(global_step) + " " + str(epoch) + " " + - str(batch)) + with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: + f.write(str(global_step)+" "+str(epoch)+ " "+ str(step)) + + writer.add_scalar(tag="eval/rec95", step=global_step, value=rec95) model.train() - + loss.backward() optimizer.step() lr_scheduler.step() optimizer.clear_grad() if global_step % args.save_steps == 0 and rank == 0: - save_dir = os.path.join(args.save_dir, - "checkpoint_{}".format(global_step)) + save_dir = os.path.join(args.save_dir, "checkpoint_{}".format(global_step)) if not os.path.exists(save_dir): os.makedirs(save_dir) save_param_path = os.path.join(save_dir, "model_state.pdparams") - paddle.save(model._layers.extractor.state_dict(), - save_param_path) + paddle.save(model._layers.encoder.state_dict(), save_param_path) tokenizer.save_pretrained(save_dir) if args.max_steps > 0 and global_step >= args.max_steps: @@ -271,24 +277,26 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader): if not os.path.exists(args.save_dir): os.makedirs(args.save_dir) - # define token for processing data - tokenizer = ppnlp.transformers.AutoTokenizer.from_pretrained( - args.extractor_name) - - trans_func = partial(convert_example, - tokenizer=tokenizer, - max_seq_length=args.max_seq_length) + # define tokenizer for processing data + tokenizer = ppnlp.transformers.AutoTokenizer.from_pretrained(args.encoder_name) + trans_func = partial( + convert_example, + tokenizer=tokenizer, + max_seq_length=args.max_seq_length + ) if args.mode == "train": start_time = time.time() + + # load data + train_ds = load_dataset(read_text_single, data_path=args.train_set_file, lazy=False) + dev_ds = load_dataset(read_text_pair, data_path=args.eval_set_file, lazy=False) + gen_tokenizer = ppnlp.transformers.AutoTokenizer.from_pretrained(args.generator_name) + dis_tokenizer = ppnlp.transformers.AutoTokenizer.from_pretrained(args.discriminator_name) + # intializing DiffCSE model - model = DiffCSE(args, tokenizer) - train_ds = load_dataset(read_text_single, - data_path=args.train_set_file, - lazy=False) - dev_ds = load_dataset(read_text_pair, - data_path=args.eval_set_file, - lazy=False) + model = DiffCSE(encoder_name=args.encoder_name, generator_name=args.generator_name, discriminator_name=args.discriminator_name, gen_tokenizer=gen_tokenizer, dis_tokenizer=dis_tokenizer, + temp=args.temp, output_emb_size=args.output_emb_size, mlm_probability=args.mlm_probability, lambda_weight=args.lambda_weight) batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input @@ -308,42 +316,41 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader): Stack(dtype="int64"), # labels ): [data for data in fn(samples)] - train_data_loader = create_dataloader(train_ds, - mode="train", - batch_size=args.batch_size, - batchify_fn=batchify_fn, - trans_fn=trans_func) - dev_data_loader = create_dataloader(dev_ds, - mode="eval", - batch_size=args.batch_size, - batchify_fn=dev_batchify_fn, - trans_fn=trans_func) - - do_train(model, tokenizer, train_data_loader, dev_data_loader) + train_data_loader = create_dataloader( + train_ds, + mode="train", + batch_size=args.batch_size, + batchify_fn=batchify_fn, + trans_fn=trans_func + ) + dev_data_loader = create_dataloader( + dev_ds, + mode="eval", + batch_size=args.batch_size, + batchify_fn=dev_batchify_fn, + trans_fn=trans_func + ) + + with LogWriter(logdir=os.path.join(args.log_dir, "scalar")) as writer: + do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=writer) end_time = time.time() print("running time {} s".format(end_time - start_time)) + if args.mode == "eval": start_time = time.time() - # initalizing Extractor model for eval - model = Extractor(args.extractor_name, - margin=args.margin, - scale=args.scale, - output_emb_size=args.output_emb_size) + # initalizing encoder model for eval + model = Encoder(args.encoder_name, temp=args.temp, output_emb_size=args.output_emb_size) # load model from saved checkpoint if args.ckpt_dir: init_from_ckpt = os.path.join(args.ckpt_dir, "model_state.pdparams") if os.path.isfile(init_from_ckpt): - print( - "*************************initializing model from {}*****************************" - .format(init_from_ckpt)) + print("*************************initializing model from {}*****************************".format(init_from_ckpt)) state_dict = paddle.load(init_from_ckpt) model.set_dict(state_dict) - dev_ds = load_dataset(read_text_pair, - data_path=args.eval_set_file, - lazy=False) + dev_ds = load_dataset(read_text_pair, data_path=args.eval_set_file, lazy=False) dev_batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input @@ -355,39 +362,33 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader): Stack(dtype="int64"), # labels ): [data for data in fn(samples)] - dev_data_loader = create_dataloader(dev_ds, - mode="eval", - batch_size=args.batch_size, - batchify_fn=dev_batchify_fn, - trans_fn=trans_func) + dev_data_loader = create_dataloader( + dev_ds, + mode="eval", + batch_size=args.batch_size, + batchify_fn=dev_batchify_fn, + trans_fn=trans_func + ) eval_res, total_num = do_eval(model, tokenizer, dev_data_loader) end_time = time.time() print("running time {} s".format(end_time - start_time)) - if args.mode == "infer": + if args.mode == "infer": start_time = time.time() - # initalizing Extractor model for eval - model = Extractor(args.extractor_name, - margin=args.margin, - scale=args.scale, - output_emb_size=args.output_emb_size) + # initalizing encoder model for eval + model = Encoder(args.encoder_name, temp=args.temp, output_emb_size=args.output_emb_size) # load model from saved checkpoint if args.ckpt_dir: init_from_ckpt = os.path.join(args.ckpt_dir, "model_state.pdparams") if os.path.isfile(init_from_ckpt): - print( - "*************************initializing model from {}*****************************" - .format(init_from_ckpt)) + print("*************************initializing model from {}*****************************".format(init_from_ckpt)) state_dict = paddle.load(init_from_ckpt) model.set_dict(state_dict) - infer_ds = load_dataset(read_text_pair, - data_path=args.infer_set_file, - lazy=False, - is_infer=True) - + infer_ds = load_dataset(read_text_pair, data_path=args.infer_set_file, lazy=False, is_infer=True) + batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # query_segment @@ -397,11 +398,13 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader): Pad(axis=0, pad_val=0), # attention_mask ): [data for data in fn(samples)] - infer_data_loader = create_dataloader(infer_ds, - mode="predict", - batch_size=args.batch_size, - batchify_fn=batchify_fn, - trans_fn=trans_func) + infer_data_loader = create_dataloader( + infer_ds, + mode="infer", + batch_size=args.batch_size, + batchify_fn=batchify_fn, + trans_fn=trans_func + ) cosin_sim = do_infer(model, tokenizer, infer_data_loader) @@ -409,6 +412,10 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader): for idx, cos in enumerate(cosin_sim): msg = "{} --> {}\n".format(idx, cos) f.write(msg) + print("Inference result has been saved to : {}".format(args.save_infer_path)) end_time = time.time() print("running time {} s".format(end_time - start_time)) + + + diff --git a/examples/text_matching/diffcse/run_eval.sh b/examples/text_matching/diffcse/run_eval.sh index f00ab73a1de2..9672cf37ef0f 100644 --- a/examples/text_matching/diffcse/run_eval.sh +++ b/examples/text_matching/diffcse/run_eval.sh @@ -1,19 +1,15 @@ -export CUDA_VISIBLE_DEVICES=0 +gpu_ids=0 +export CUDA_VISIBLE_DEVICES=${gpu_ids} -python run_diffcse.py \ +log_dir="log_eval" +python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ + run_diffcse.py \ --mode "eval" \ - --extractor_name "rocketqa-zh-dureader-query-encoder" \ - --generator_name "ernie-1.0" \ - --discriminator_name "ernie-1.0" \ + --encoder_name "rocketqa-zh-dureader-query-encoder" \ --max_seq_length "128" \ --output_emb_size "32" \ - --eval_set_file ./data/test_v1.txt \ - --ckpt_dir ./checkpoints/checkpoint_10000 \ + --eval_set_file "../diffcse/data/test_v1.txt" \ + --ckpt_dir "./checkpoints_diffcse_ErG3D3/best_spearman" \ --batch_size "8" \ - --dropout "0.1" \ - --margin "0.0" \ - --scale "20" \ - --dup_rate "0.0" \ --seed "0" \ --device "gpu" - diff --git a/examples/text_matching/diffcse/run_infer.sh b/examples/text_matching/diffcse/run_infer.sh index 9914aa894b04..db808114e370 100644 --- a/examples/text_matching/diffcse/run_infer.sh +++ b/examples/text_matching/diffcse/run_infer.sh @@ -1,20 +1,16 @@ -export CUDA_VISIBLE_DEVICES=0 +gpu_ids=0 +export CUDA_VISIBLE_DEVICES=${gpu_ids} - -python run_diffcse.py \ +log_dir="log_infer" +python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ + run_diffcse.py \ --mode "infer" \ - --extractor_name "rocketqa-zh-dureader-query-encoder" \ - --generator_name "ernie-1.0" \ - --discriminator_name "ernie-1.0" \ + --encoder_name "rocketqa-zh-dureader-query-encoder" \ --max_seq_length "128" \ --output_emb_size "32" \ - --infer_set_file "./data/test.txt" \ - --ckpt_dir "./checkpoints/checkpoint_10000" \ + --infer_set_file "../diffcse/data/test.txt" \ + --ckpt_dir "./checkpoints_diffcse_ErG3D3/best_spearman" \ --save_infer_path "./infer_result.txt" \ --batch_size "8" \ - --dropout "0.1" \ - --margin "0.0" \ - --scale "20" \ - --dup_rate "0.0" \ --seed "0" \ --device "gpu" diff --git a/examples/text_matching/diffcse/run_train.sh b/examples/text_matching/diffcse/run_train.sh index 8fa347e70265..9d6fd14e299e 100644 --- a/examples/text_matching/diffcse/run_train.sh +++ b/examples/text_matching/diffcse/run_train.sh @@ -1,28 +1,27 @@ -gpu_ids=0,1,2,3,4,5,6,7 +gpu_ids=0 export CUDA_VISIBLE_DEVICES=${gpu_ids} - -python -u -m paddle.distributed.launch --gpus ${gpu_ids} \ +log_dir="log_diffcse_ErG3D3" +python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ run_diffcse.py \ --mode "train" \ - --extractor_name "rocketqa-zh-dureader-query-encoder" \ - --generator_name "ernie-1.0" \ - --discriminator_name "ernie-1.0" \ + --encoder_name "rocketqa-zh-dureader-query-encoder" \ + --generator_name "ernie-3.0-base-zh" \ + --discriminator_name "ernie-3.0-base-zh" \ --max_seq_length "128" \ --output_emb_size "32" \ - --train_set_file ./data/train.txt \ - --eval_set_file ./data/test_v1.txt \ - --save_dir ./checkpoints \ - --save_steps "10000" \ + --train_set_file "../dataset/100w/train_webdata.txt" \ + --eval_set_file "../dataset/100w/test_v1.txt" \ + --save_dir "./checkpoints_diffcse_ErG3D3" \ + --log_dir ${log_dir} \ + --save_steps "50000" \ --eval_steps "1000" \ --batch_size "32" \ - --epochs "5" \ + --epochs "3" \ + --mlm_probability "0.15" \ + --lambda_weight "0.15" \ --learning_rate "3e-5" \ --weight_decay "0.01" \ --warmup_proportion "0.01" \ - --dropout "0.1" \ - --margin "0.0" \ - --scale "20" \ - --dup_rate "0.0" \ --seed "0" \ - --device "gpu" \ No newline at end of file + --device "gpu" diff --git a/examples/text_matching/diffcse/test_train.sh b/examples/text_matching/diffcse/test_train.sh deleted file mode 100644 index 3d4c1f2c09fc..000000000000 --- a/examples/text_matching/diffcse/test_train.sh +++ /dev/null @@ -1,25 +0,0 @@ -export CUDA_VISIBLE_DEVICES=0 - -python run_diffcse.py \ - --mode "train" \ - --extractor_name "rocketqa-zh-dureader-query-encoder" \ - --generator_name "ernie-3.0-mini-zh" \ - --discriminator_name "ernie-3.0-base-zh" \ - --max_seq_length "128" \ - --output_emb_size "32" \ - --train_set_file ./data/train.txt \ - --eval_set_file ./data/test_v1.txt \ - --save_dir ./checkpoints \ - --save_steps "10000" \ - --eval_steps "10" \ - --batch_size "2" \ - --epochs "5" \ - --learning_rate "3e-5" \ - --weight_decay "0.01" \ - --warmup_proportion "0.01" \ - --dropout "0.1" \ - --margin "0.0" \ - --scale "20" \ - --dup_rate "0.0" \ - --seed "0" \ - --device "gpu" diff --git a/examples/text_matching/diffcse/utils.py b/examples/text_matching/diffcse/utils.py index 6fa7dcb9fcdc..8ef350bac183 100644 --- a/examples/text_matching/diffcse/utils.py +++ b/examples/text_matching/diffcse/utils.py @@ -12,18 +12,17 @@ # See the License for the specific language governing permissions and # limitations under the License. + import random import numpy as np import paddle - def set_seed(seed=0): random.seed(seed) np.random.seed(seed) paddle.seed(seed) - def masked_fill(x, mask, value): y = paddle.full(x.shape, value, x.dtype) return paddle.where(mask, y, x) From a83a902393e7ad0172c7a94523b0f6fae23b78e9 Mon Sep 17 00:00:00 2001 From: milen <1649759610@qq.com> Date: Mon, 11 Jul 2022 11:37:21 +0000 Subject: [PATCH 008/116] initial commit --- examples/text_matching/diffcse/README.md | 80 ++--- .../text_matching/diffcse/custom_ernie.py | 3 +- examples/text_matching/diffcse/data.py | 68 ++-- .../text_matching/diffcse/eval_metrics.py | 87 +++-- examples/text_matching/diffcse/model.py | 243 ++++++++----- examples/text_matching/diffcse/run_diffcse.py | 324 +++++++++++------- examples/text_matching/diffcse/utils.py | 3 +- 7 files changed, 503 insertions(+), 305 deletions(-) diff --git a/examples/text_matching/diffcse/README.md b/examples/text_matching/diffcse/README.md index f476101ccb2b..114026eeeb75 100644 --- a/examples/text_matching/diffcse/README.md +++ b/examples/text_matching/diffcse/README.md @@ -29,28 +29,28 @@ export CUDA_VISIBLE_DEVICES=${gpu_ids} log_dir="log_diffcse_ErG3D3" python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ - run_diffcse.py \ - --mode "train" \ - --encoder_name "rocketqa-zh-dureader-query-encoder" \ - --generator_name "ernie-3.0-base-zh" \ - --discriminator_name "ernie-3.0-base-zh" \ - --max_seq_length "128" \ - --output_emb_size "32" \ - --train_set_file "../dataset/100w/train_webdata.txt" \ - --eval_set_file "../dataset/100w/test_v1.txt" \ - --save_dir "./checkpoints_diffcse_ErG3D3" \ - --log_dir ${log_dir} \ - --save_steps "50000" \ - --eval_steps "1000" \ - --batch_size "32" \ - --epochs "3" \ - --mlm_probability "0.15" \ - --lambda_weight "0.15" \ - --learning_rate "3e-5" \ - --weight_decay "0.01" \ - --warmup_proportion "0.01" \ - --seed "0" \ - --device "gpu" + run_diffcse.py \ + --mode "train" \ + --encoder_name "rocketqa-zh-dureader-query-encoder" \ + --generator_name "ernie-3.0-base-zh" \ + --discriminator_name "ernie-3.0-base-zh" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --train_set_file "../dataset/100w/train_webdata.txt" \ + --eval_set_file "../dataset/100w/test_v1.txt" \ + --save_dir "./checkpoints_diffcse_ErG3D3" \ + --log_dir ${log_dir} \ + --save_steps "50000" \ + --eval_steps "1000" \ + --batch_size "32" \ + --epochs "3" \ + --mlm_probability "0.15" \ + --lambda_weight "0.15" \ + --learning_rate "3e-5" \ + --weight_decay "0.01" \ + --warmup_proportion "0.01" \ + --seed "0" \ + --device "gpu" ``` 可支持配置的参数： @@ -97,15 +97,15 @@ export CUDA_VISIBLE_DEVICES=${gpu_ids} log_dir="log_eval" python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ run_diffcse.py \ - --mode "eval" \ - --encoder_name "rocketqa-zh-dureader-query-encoder" \ - --max_seq_length "128" \ - --output_emb_size "32" \ - --eval_set_file "../diffcse/data/test_v1.txt" \ - --ckpt_dir "./checkpoints_diffcse_ErG3D3/best_spearman" \ - --batch_size "8" \ - --seed "0" \ - --device "gpu" + --mode "eval" \ + --encoder_name "rocketqa-zh-dureader-query-encoder" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --eval_set_file "../diffcse/data/test_v1.txt" \ + --ckpt_dir "./checkpoints_diffcse_ErG3D3/best_spearman" \ + --batch_size "8" \ + --seed "0" \ + --device "gpu" ``` 可支持配置的参数： @@ -123,16 +123,16 @@ export CUDA_VISIBLE_DEVICES=${gpu_ids} log_dir="log_infer" python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ run_diffcse.py \ - --mode "infer" \ - --encoder_name "rocketqa-zh-dureader-query-encoder" \ - --max_seq_length "128" \ - --output_emb_size "32" \ - --infer_set_file "../diffcse/data/test.txt" \ - --ckpt_dir "./checkpoints_diffcse_ErG3D3/best_spearman" \ + --mode "infer" \ + --encoder_name "rocketqa-zh-dureader-query-encoder" \ + --max_seq_length "128" \ + --output_emb_size "32" \ + --infer_set_file "../diffcse/data/test.txt" \ + --ckpt_dir "./checkpoints_diffcse_ErG3D3/best_spearman" \ --save_infer_path "./infer_result.txt" \ - --batch_size "8" \ - --seed "0" \ - --device "gpu" + --batch_size "8" \ + --seed "0" \ + --device "gpu" ``` diff --git a/examples/text_matching/diffcse/custom_ernie.py b/examples/text_matching/diffcse/custom_ernie.py index 0c0c910feb38..d31e67ab523c 100644 --- a/examples/text_matching/diffcse/custom_ernie.py +++ b/examples/text_matching/diffcse/custom_ernie.py @@ -648,7 +648,8 @@ def forward(self, task_type_ids=task_type_ids) if cls_input is not None: - embedding_output = paddle.concat([cls_input.unsqueeze(1), embedding_output[:, 1:, :]], axis=1) + embedding_output = paddle.concat( + [cls_input.unsqueeze(1), embedding_output[:, 1:, :]], axis=1) encoder_outputs = self.encoder(embedding_output, attention_mask) sequence_output = encoder_outputs diff --git a/examples/text_matching/diffcse/data.py b/examples/text_matching/diffcse/data.py index 96ffafdb6e8a..5ecc7c02f73a 100644 --- a/examples/text_matching/diffcse/data.py +++ b/examples/text_matching/diffcse/data.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - import paddle import os @@ -23,25 +22,39 @@ def get_special_tokens(): return ["[PAD]", "[CLS]", "[SEP]", "[MASK]", "[UNK]"] + def get_special_token_ids(tokenizer): special_tokens = ["[PAD]", "[CLS]", "[SEP]", "[MASK]", "[UNK]"] return tokenizer.convert_tokens_to_ids(special_tokens) + def get_special_token_dict(tokenizer): special_tokens = ["[PAD]", "[CLS]", "[SEP]", "[MASK]", "[UNK]"] - special_token_dict = dict(zip(special_tokens, tokenizer.convert_tokens_to_ids(special_tokens))) + special_token_dict = dict( + zip(special_tokens, tokenizer.convert_tokens_to_ids(special_tokens))) return special_token_dict -def create_dataloader(dataset, mode="train", batch_size=1, batchify_fn=None, trans_fn=None): +def create_dataloader(dataset, + mode="train", + batch_size=1, + batchify_fn=None, + trans_fn=None): if trans_fn: dataset = dataset.map(trans_fn) shuffle = True if mode == "train" else False if mode == "train": - batch_sampler = paddle.io.DistributedBatchSampler(dataset, batch_size=batch_size, shuffle=shuffle) + batch_sampler = paddle.io.DistributedBatchSampler(dataset, + batch_size=batch_size, + shuffle=shuffle) else: - batch_sampler = paddle.io.BatchSampler(dataset, batch_size=batch_size, shuffle=shuffle) - return paddle.io.DataLoader(dataset=dataset, batch_sampler=batch_sampler, collate_fn=batchify_fn, return_list=True) + batch_sampler = paddle.io.BatchSampler(dataset, + batch_size=batch_size, + shuffle=shuffle) + return paddle.io.DataLoader(dataset=dataset, + batch_sampler=batch_sampler, + collate_fn=batchify_fn, + return_list=True) def convert_example(example, tokenizer, max_seq_length=512, do_evalute=False): @@ -52,7 +65,9 @@ def convert_example(example, tokenizer, max_seq_length=512, do_evalute=False): result += [example["label"]] else: # do_train - encoded_inputs = tokenizer(text=text, max_seq_len=max_seq_length, return_attention_mask=True) + encoded_inputs = tokenizer(text=text, + max_seq_len=max_seq_length, + return_attention_mask=True) input_ids = encoded_inputs["input_ids"] token_type_ids = encoded_inputs["token_type_ids"] attention_mask = encoded_inputs["attention_mask"] @@ -66,10 +81,12 @@ def read_text_single(data_path): data = line.rstrip() yield {"text_a": data, "text_b": data} + def masked_fill(x, mask, value): y = paddle.full(x.shape, value, x.dtype) return paddle.where(mask, y, x) + def mask_tokens(batch_inputs, tokenizer, mlm_probability=0.15): """ Description: Mask input_ids for masked language modeling: 80% MASK, 10% random, 10% original @@ -78,24 +95,34 @@ def mask_tokens(batch_inputs, tokenizer, mlm_probability=0.15): mlm_labels = batch_inputs.clone() probability_matrix = paddle.full(mlm_inputs.shape, mlm_probability) - - special_tokens_mask = paddle.cast(paddle.zeros(mlm_inputs.shape), dtype=bool) + + special_tokens_mask = paddle.cast(paddle.zeros(mlm_inputs.shape), + dtype=bool) for special_token_id in get_special_token_ids(tokenizer): - special_tokens_mask |= (mlm_inputs==special_token_id) + special_tokens_mask |= (mlm_inputs == special_token_id) - - probability_matrix = masked_fill(probability_matrix, special_tokens_mask, 0.0) + probability_matrix = masked_fill(probability_matrix, special_tokens_mask, + 0.0) - masked_indices = paddle.cast(paddle.bernoulli(probability_matrix), dtype=bool) + masked_indices = paddle.cast(paddle.bernoulli(probability_matrix), + dtype=bool) mlm_labels = masked_fill(mlm_labels, ~masked_indices, -100) # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) - indices_replaced = paddle.cast(paddle.bernoulli(paddle.full(mlm_inputs.shape, 0.8)), dtype=bool) & masked_indices - mlm_inputs = masked_fill(mlm_inputs, indices_replaced, tokenizer.mask_token_id) + indices_replaced = paddle.cast(paddle.bernoulli( + paddle.full(mlm_inputs.shape, 0.8)), + dtype=bool) & masked_indices + mlm_inputs = masked_fill(mlm_inputs, indices_replaced, + tokenizer.mask_token_id) # 10% of the time, we replace masked input tokens with random word - indices_random = paddle.cast(paddle.bernoulli(paddle.full(mlm_inputs.shape, 0.5)) ,dtype=bool) & masked_indices & ~indices_replaced - random_words = paddle.randint(0, len(tokenizer), mlm_inputs.shape, dtype=mlm_inputs.dtype) + indices_random = paddle.cast( + paddle.bernoulli(paddle.full(mlm_inputs.shape, 0.5)), + dtype=bool) & masked_indices & ~indices_replaced + random_words = paddle.randint(0, + len(tokenizer), + mlm_inputs.shape, + dtype=mlm_inputs.dtype) mlm_inputs = paddle.where(indices_random, random_words, mlm_inputs) # The rest of the time (10% of the time) we keep the masked input tokens unchanged @@ -133,7 +160,8 @@ def word_repetition(input_ids, token_type_ids, dup_rate=0.32): if (actual_len > 5): dup_len = random.randint(a=0, b=max(2, int(dup_rate * actual_len))) # Skip cls and sep position - dup_word_index = random.sample(list(range(1, actual_len - 1)), k=dup_len) + dup_word_index = random.sample(list(range(1, actual_len - 1)), + k=dup_len) r_input_id = [] r_token_type_id = [] @@ -156,5 +184,5 @@ def word_repetition(input_ids, token_type_ids, dup_rate=0.32): repetitied_input_ids[batch_id] += [0] * pad_len repetitied_token_type_ids[batch_id] += [0] * pad_len - return paddle.to_tensor(repetitied_input_ids), paddle.to_tensor(repetitied_token_type_ids) - + return paddle.to_tensor(repetitied_input_ids), paddle.to_tensor( + repetitied_token_type_ids) diff --git a/examples/text_matching/diffcse/eval_metrics.py b/examples/text_matching/diffcse/eval_metrics.py index c6b3ea9225d4..b4695a47c30d 100644 --- a/examples/text_matching/diffcse/eval_metrics.py +++ b/examples/text_matching/diffcse/eval_metrics.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - import numpy as np from scipy import stats from sklearn import metrics @@ -25,16 +24,21 @@ def eval_metrics(labels, sims): eval_res["spearman_corr"] = spearman_corr print("Spearman corr --> {:.5f}\n".format(spearman_corr)) - labels_2_cls = np.where(np.greater_equal(labels, 3), np.ones_like(labels), np.zeros_like(labels)) + labels_2_cls = np.where(np.greater_equal(labels, 3), np.ones_like(labels), + np.zeros_like(labels)) - pre, rec, thr = metrics.precision_recall_curve(labels_2_cls, sims, pos_label=1) + pre, rec, thr = metrics.precision_recall_curve(labels_2_cls, + sims, + pos_label=1) pre_rec = np.sum([pre, rec], axis=0) best_pre_rec = np.max(pre_rec) best_pre = pre[np.argmax(pre_rec)] best_rec = rec[np.argmax(pre_rec)] best_thr = thr[np.argmax(pre_rec)] eval_res["best_pre_rec_thr"] = [best_pre_rec, best_pre, best_rec, best_thr] - print("2-CLS best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}".format(best_pre_rec, best_pre, best_rec, best_thr)) + print( + "2-CLS best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}" + .format(best_pre_rec, best_pre, best_rec, best_thr)) pre_rec_thr = list(zip(pre, rec, thr)) pre_rec_thr.sort(key=lambda x: x[1], reverse=True) @@ -45,20 +49,22 @@ def eval_metrics(labels, sims): pre_rec_thr_at_K = x break eval_res["best_pre_rec_thr_at_K"] = pre_rec_thr_at_K - print("2-CLS best pre@{:.2f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}".format(at_K, pre_rec_thr_at_K[0], pre_rec_thr_at_K[1], pre_rec_thr_at_K[2])) + print( + "2-CLS best pre@{:.2f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}". + format(at_K, pre_rec_thr_at_K[0], pre_rec_thr_at_K[1], + pre_rec_thr_at_K[2])) - preds_2_cls = np.where(np.greater_equal(sims, best_thr), np.ones_like(sims), np.zeros_like(sims)) + preds_2_cls = np.where(np.greater_equal(sims, best_thr), np.ones_like(sims), + np.zeros_like(sims)) acc_2_cls = metrics.accuracy_score(labels_2_cls, preds_2_cls) auc_2_cls = metrics.roc_auc_score(labels_2_cls, sims) - report_2_cls = metrics.classification_report( - labels_2_cls, - preds_2_cls, - labels=[0, 1], - target_names=["0", "1"], - digits=5, - output_dict=True - ) + report_2_cls = metrics.classification_report(labels_2_cls, + preds_2_cls, + labels=[0, 1], + target_names=["0", "1"], + digits=5, + output_dict=True) eval_res["acc_2_cls"] = acc_2_cls eval_res["auc_2_cls"] = auc_2_cls eval_res["report_2_cls"] = report_2_cls @@ -66,21 +72,32 @@ def eval_metrics(labels, sims): print("2-CLS accuracy --> {}".format(acc_2_cls)) print("2-CLS roc-auc --> {}".format(auc_2_cls)) print("\t".join(["label", "precision", "recall", "f1-score", "support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("2-CLS 0", report_2_cls["0"]["precision"], report_2_cls["0"]["recall"], report_2_cls["0"]["f1-score"], report_2_cls["0"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}\n".format("2-CLS 1", report_2_cls["1"]["precision"], report_2_cls["1"]["recall"], report_2_cls["1"]["f1-score"], report_2_cls["1"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( + "2-CLS 0", report_2_cls["0"]["precision"], report_2_cls["0"]["recall"], + report_2_cls["0"]["f1-score"], report_2_cls["0"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}\n".format( + "2-CLS 1", report_2_cls["1"]["precision"], report_2_cls["1"]["recall"], + report_2_cls["1"]["f1-score"], report_2_cls["1"]["support"])) best_thr_6_cls = [] for pivot in [5, 4, 3, 2, 1]: - labels_pivot = np.where(np.greater_equal(labels, pivot), np.ones_like(labels), np.zeros_like(labels)) - pre_pivot, rec_pivot, thr_pivot = metrics.precision_recall_curve(labels_pivot, sims, pos_label=1) + labels_pivot = np.where(np.greater_equal(labels, pivot), + np.ones_like(labels), np.zeros_like(labels)) + pre_pivot, rec_pivot, thr_pivot = metrics.precision_recall_curve( + labels_pivot, sims, pos_label=1) pre_rec_pivot = np.sum([pre_pivot, rec_pivot], axis=0) best_pre_rec_pivot = np.max(pre_rec_pivot) best_pre_pivot = pre_pivot[np.argmax(pre_rec_pivot)] best_rec_pivot = rec_pivot[np.argmax(pre_rec_pivot)] best_thr_pivot = thr_pivot[np.argmax(pre_rec_pivot)] best_thr_6_cls.append(best_thr_pivot) - eval_res["best_pre_rec_thr_6_cls_at_{}_pivot".format(pivot)] = [best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, best_thr_pivot] - print("6-CLS [split at {} pivot] best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}".format(pivot, best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, best_thr_pivot)) + eval_res["best_pre_rec_thr_6_cls_at_{}_pivot".format(pivot)] = [ + best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, best_thr_pivot + ] + print( + "6-CLS [split at {} pivot] best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}" + .format(pivot, best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, + best_thr_pivot)) preds = [] for s in sims: @@ -98,19 +115,31 @@ def eval_metrics(labels, sims): labels=[0, 1, 2, 3, 4, 5], target_names=["0", "1", "2", "3", "4", "5"], digits=5, - output_dict=True - ) + output_dict=True) eval_res["acc_6_cls"] = acc_6_cls eval_res["report_6_cls"] = report_6_cls - print("6-CLS metrics are evaluated on the best thr --> {}".format(best_thr_6_cls)) + print("6-CLS metrics are evaluated on the best thr --> {}".format( + best_thr_6_cls)) print("6-CLS accuracy --> {}".format(acc_6_cls)) print("\t".join(["label", "precision", "recall", "f1-score", "support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 0", report_6_cls["0"]["precision"], report_6_cls["0"]["recall"], report_6_cls["0"]["f1-score"], report_6_cls["0"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 1", report_6_cls["1"]["precision"], report_6_cls["1"]["recall"], report_6_cls["1"]["f1-score"], report_6_cls["1"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 2", report_6_cls["2"]["precision"], report_6_cls["2"]["recall"], report_6_cls["2"]["f1-score"], report_6_cls["2"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 3", report_6_cls["3"]["precision"], report_6_cls["3"]["recall"], report_6_cls["3"]["f1-score"], report_6_cls["3"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 4", report_6_cls["4"]["precision"], report_6_cls["4"]["recall"], report_6_cls["4"]["f1-score"], report_6_cls["4"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format("6-CLS 5", report_6_cls["5"]["precision"], report_6_cls["5"]["recall"], report_6_cls["5"]["f1-score"], report_6_cls["5"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( + "6-CLS 0", report_6_cls["0"]["precision"], report_6_cls["0"]["recall"], + report_6_cls["0"]["f1-score"], report_6_cls["0"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( + "6-CLS 1", report_6_cls["1"]["precision"], report_6_cls["1"]["recall"], + report_6_cls["1"]["f1-score"], report_6_cls["1"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( + "6-CLS 2", report_6_cls["2"]["precision"], report_6_cls["2"]["recall"], + report_6_cls["2"]["f1-score"], report_6_cls["2"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( + "6-CLS 3", report_6_cls["3"]["precision"], report_6_cls["3"]["recall"], + report_6_cls["3"]["f1-score"], report_6_cls["3"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( + "6-CLS 4", report_6_cls["4"]["precision"], report_6_cls["4"]["recall"], + report_6_cls["4"]["f1-score"], report_6_cls["4"]["support"])) + print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( + "6-CLS 5", report_6_cls["5"]["precision"], report_6_cls["5"]["recall"], + report_6_cls["5"]["f1-score"], report_6_cls["5"]["support"])) return eval_res diff --git a/examples/text_matching/diffcse/model.py b/examples/text_matching/diffcse/model.py index fc7366901cc5..300b538bef8c 100644 --- a/examples/text_matching/diffcse/model.py +++ b/examples/text_matching/diffcse/model.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - import numpy as np import paddle @@ -23,27 +22,34 @@ from data import mask_tokens from custom_ernie import ErnieModel as CustomErnie + class ProjectionMLP(nn.Layer): + def __init__(self, in_dim): super(ProjectionMLP, self).__init__() hidden_dim = in_dim * 2 out_dim = in_dim - affine=False - list_layers = [nn.Linear(in_dim, hidden_dim, bias_attr=False), - nn.BatchNorm1D(hidden_dim), - nn.ReLU()] - list_layers += [nn.Linear(hidden_dim, out_dim, bias_attr=False), - nn.BatchNorm1D(out_dim)] + affine = False + list_layers = [ + nn.Linear(in_dim, hidden_dim, bias_attr=False), + nn.BatchNorm1D(hidden_dim), + nn.ReLU() + ] + list_layers += [ + nn.Linear(hidden_dim, out_dim, bias_attr=False), + nn.BatchNorm1D(out_dim) + ] self.net = nn.Sequential(*list_layers) def forward(self, x): return self.net(x) + class Similarity(nn.Layer): """ Dot product or cosine similarity """ - + def __init__(self, temp): super(Similarity, self).__init__() self.temp = temp @@ -51,24 +57,26 @@ def __init__(self, temp): self.record = None self.pos_avg = 0.0 self.neg_avg = 0.0 - + def forward(self, x, y, one_vs_one=False): if one_vs_one: sim = self.cos(x, y) return sim - - x=x.unsqueeze(1) - y=y.unsqueeze(0) + + x = x.unsqueeze(1) + y = y.unsqueeze(0) sim = self.cos(x, y) self.record = sim.detach() min_size = min(self.record.shape[0], self.record.shape[1]) num_item = self.record.shape[0] * self.record.shape[1] self.pos_avg = paddle.diag(self.record).sum().item() / min_size - self.neg_avg = (self.record.sum().item() - paddle.diag(self.record).sum().item()) / (num_item-min_size) + self.neg_avg = (self.record.sum().item() - paddle.diag( + self.record).sum().item()) / (num_item - min_size) return sim / self.temp class Encoder(nn.Layer): + def __init__(self, pretrained_model_name, temp=0.05, output_emb_size=None): super(Encoder, self).__init__() self.ptm = AutoModel.from_pretrained(pretrained_model_name) @@ -77,82 +85,83 @@ def __init__(self, pretrained_model_name, temp=0.05, output_emb_size=None): self.mlp = ProjectionMLP(self.ptm.config['hidden_size']) if output_emb_size is not None: - self.emb_reduce_linear = nn.Linear(self.ptm.config['hidden_size'], output_emb_size) + self.emb_reduce_linear = nn.Linear(self.ptm.config['hidden_size'], + output_emb_size) self.temp = temp self.sim = Similarity(temp) - def get_pooled_embedding(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, with_pooler=False): + def get_pooled_embedding(self, + input_ids, + token_type_ids=None, + position_ids=None, + attention_mask=None, + with_pooler=False): # Note: cls_embedding is poolerd embedding with act tanh - sequence_output, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids, attention_mask) + sequence_output, cls_embedding = self.ptm(input_ids, token_type_ids, + position_ids, attention_mask) if not with_pooler: ori_cls_embedding = sequence_output[:, 0, :] else: ori_cls_embedding = cls_embedding - + mlp_cls_embedding = self.mlp(ori_cls_embedding) if self.output_emb_size is not None: cls_embedding = self.emb_reduce_linear(mlp_cls_embedding) return cls_embedding, mlp_cls_embedding - def cosine_sim( - self, - query_input_ids, - key_input_ids, - query_token_type_ids=None, - query_position_ids=None, - query_attention_mask=None, - key_token_type_ids=None, - key_position_ids=None, - key_attention_mask=None, - with_pooler=False - ): + def cosine_sim(self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + query_position_ids=None, + query_attention_mask=None, + key_token_type_ids=None, + key_position_ids=None, + key_attention_mask=None, + with_pooler=False): query_cls_embedding, _ = self.get_pooled_embedding( query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask, - with_pooler=with_pooler - ) + with_pooler=with_pooler) key_cls_embedding, _ = self.get_pooled_embedding( key_input_ids, key_token_type_ids, key_position_ids, key_attention_mask, - with_pooler=with_pooler - ) + with_pooler=with_pooler) - cosine_sim = self.sim(query_cls_embedding, key_cls_embedding, one_vs_one=True) + cosine_sim = self.sim(query_cls_embedding, + key_cls_embedding, + one_vs_one=True) # cosine_sim = paddle.sum(query_cls_embedding * key_cls_embedding, axis=-1) return cosine_sim - def forward( - self, - query_input_ids, - key_input_ids, - query_token_type_ids=None, - query_position_ids=None, - query_attention_mask=None, - key_token_type_ids=None, - key_position_ids=None, - key_attention_mask=None, - with_pooler=False - ): + def forward(self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + query_position_ids=None, + query_attention_mask=None, + key_token_type_ids=None, + key_position_ids=None, + key_attention_mask=None, + with_pooler=False): query_cls_embedding, mlp_query_cls_embedding = self.get_pooled_embedding( query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask, - with_pooler=with_pooler - ) + with_pooler=with_pooler) key_cls_embedding, mlp_key_cls_embedding = self.get_pooled_embedding( key_input_ids, key_token_type_ids, key_position_ids, key_attention_mask, - with_pooler=with_pooler - ) + with_pooler=with_pooler) cosine_sim = self.sim(query_cls_embedding, key_cls_embedding) @@ -160,17 +169,28 @@ def forward( labels = paddle.reshape(labels, shape=[-1, 1]) loss = F.cross_entropy(input=cosine_sim, label=labels) - mlp_cls_embedding = paddle.concat([mlp_query_cls_embedding, mlp_key_cls_embedding], axis=0) + mlp_cls_embedding = paddle.concat( + [mlp_query_cls_embedding, mlp_key_cls_embedding], axis=0) return loss, mlp_cls_embedding + class Discriminator(nn.Layer): + def __init__(self, ptm_model_name): super(Discriminator, self).__init__() self.ptm = CustomErnie.from_pretrained(ptm_model_name) self.classifier = nn.Linear(self.ptm.config["hidden_size"], 2) - - def forward(self, input_ids, labels, cls_input, token_type_ids=None, attention_mask=None): - sequence_output, _ = self.ptm(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, cls_input=cls_input) + + def forward(self, + input_ids, + labels, + cls_input, + token_type_ids=None, + attention_mask=None): + sequence_output, _ = self.ptm(input_ids, + token_type_ids=token_type_ids, + attention_mask=attention_mask, + cls_input=cls_input) pred_scores = self.classifier(sequence_output) loss = F.cross_entropy(input=pred_scores, label=labels) @@ -178,7 +198,17 @@ def forward(self, input_ids, labels, cls_input, token_type_ids=None, attention_m class DiffCSE(nn.Layer): - def __init__(self, encoder_name, generator_name, discriminator_name, gen_tokenizer, dis_tokenizer, temp=0.05, output_emb_size=32, mlm_probability=0.15, lambda_weight=0.15): + + def __init__(self, + encoder_name, + generator_name, + discriminator_name, + gen_tokenizer, + dis_tokenizer, + temp=0.05, + output_emb_size=32, + mlm_probability=0.15, + lambda_weight=0.15): super(DiffCSE, self).__init__() self.encoder_name = encoder_name self.generator_name = generator_name @@ -190,7 +220,9 @@ def __init__(self, encoder_name, generator_name, discriminator_name, gen_tokeniz self.mlm_probability = mlm_probability self.lambda_weight = lambda_weight - self.encoder = Encoder(encoder_name, temp=temp, output_emb_size=output_emb_size) + self.encoder = Encoder(encoder_name, + temp=temp, + output_emb_size=output_emb_size) self.generator = ErnieForMaskedLM.from_pretrained(generator_name) self.discriminator = Discriminator(discriminator_name) @@ -198,26 +230,35 @@ def __init__(self, encoder_name, generator_name, discriminator_name, gen_tokeniz self.rtd_rep_acc = 0.0 self.rtd_fix_acc = 0.0 - def train_forward(self, - query_input_ids, - key_input_ids, - query_token_type_ids=None, - key_token_type_ids=None, - query_attention_mask=None, - key_attention_mask=None - ): + def train_forward(self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + key_token_type_ids=None, + query_attention_mask=None, + key_attention_mask=None): # extract senmantic vector with encoder and then comput CL loss - loss, mlp_cls_embedding = self.encoder(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask) + loss, mlp_cls_embedding = self.encoder( + query_input_ids, + key_input_ids, + query_token_type_ids=query_token_type_ids, + key_token_type_ids=key_token_type_ids, + query_attention_mask=query_attention_mask, + key_attention_mask=key_attention_mask) - with paddle.no_grad(): # mask tokens for query and key input_ids and then predict mask token with generator input_ids = paddle.concat([query_input_ids, key_input_ids], axis=0) - attention_mask = paddle.concat([query_attention_mask, key_attention_mask], axis=0) - mlm_input_ids, _ = mask_tokens(paddle.concat([query_input_ids, key_input_ids], axis=0), self.gen_tokenizer, mlm_probability=self.mlm_probability) + attention_mask = paddle.concat( + [query_attention_mask, key_attention_mask], axis=0) + mlm_input_ids, _ = mask_tokens(paddle.concat( + [query_input_ids, key_input_ids], axis=0), + self.gen_tokenizer, + mlm_probability=self.mlm_probability) # predict tokens using generator - pred_tokens = self.generator(mlm_input_ids, attention_mask=attention_mask).argmax(axis=-1) + pred_tokens = self.generator( + mlm_input_ids, attention_mask=attention_mask).argmax(axis=-1) pred_tokens = pred_tokens.detach() @@ -229,50 +270,68 @@ def train_forward(self, e_inputs = pred_tokens * attention_mask replaced = pred_tokens != input_ids e_labels = paddle.cast(replaced, dtype="int64") * attention_mask - rtd_loss, prediction = self.discriminator(e_inputs, e_labels, cls_input=mlp_cls_embedding) + rtd_loss, prediction = self.discriminator(e_inputs, + e_labels, + cls_input=mlp_cls_embedding) loss = loss + self.lambda_weight * rtd_loss rep = (e_labels == 1) * attention_mask fix = (e_labels == 0) * attention_mask self.rtd_rep_acc = float((prediction * rep).sum() / rep.sum()) self.rtd_fix_acc = float(1.0 - (prediction * fix).sum() / fix.sum()) - self.rtd_acc = float(((prediction == e_labels) * attention_mask).sum()/attention_mask.sum()) + self.rtd_acc = float(((prediction == e_labels) * attention_mask).sum() / + attention_mask.sum()) return loss, rtd_loss - + def encode_by_discriminator(self, batch_tokens): new_tokens = [] for one_tokens in batch_tokens: - one_gen_tokens = self.gen_tokenizer.convert_ids_to_tokens(one_tokens.tolist()) - new_tokens.append(self.dis_tokenizer.convert_tokens_to_ids(one_gen_tokens)) - + one_gen_tokens = self.gen_tokenizer.convert_ids_to_tokens( + one_tokens.tolist()) + new_tokens.append( + self.dis_tokenizer.convert_tokens_to_ids(one_gen_tokens)) + return paddle.to_tensor(new_tokens) - - def test_forward(self, - query_input_ids, - key_input_ids, - query_token_type_ids=None, - key_token_type_ids=None, - query_attention_mask=None, - key_attention_mask=None - ): + def test_forward(self, + query_input_ids, + key_input_ids, + query_token_type_ids=None, + key_token_type_ids=None, + query_attention_mask=None, + key_attention_mask=None): # compute cosine similarity for query and key text - cos_sim = self.encoder.cosine_sim(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask) + cos_sim = self.encoder.cosine_sim( + query_input_ids, + key_input_ids, + query_token_type_ids=query_token_type_ids, + key_token_type_ids=key_token_type_ids, + query_attention_mask=query_attention_mask, + key_attention_mask=key_attention_mask) return cos_sim - def forward(self, - query_input_ids, + def forward(self, + query_input_ids, key_input_ids, - query_token_type_ids=None, + query_token_type_ids=None, key_token_type_ids=None, query_attention_mask=None, key_attention_mask=None, - mode="train" - ): + mode="train"): if mode == "train": - return self.train_forward(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask) + return self.train_forward(query_input_ids, + key_input_ids, + query_token_type_ids=query_token_type_ids, + key_token_type_ids=key_token_type_ids, + query_attention_mask=query_attention_mask, + key_attention_mask=key_attention_mask) else: - return self.test_forward(query_input_ids, key_input_ids, query_token_type_ids=query_token_type_ids, key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask) + return self.test_forward(query_input_ids, + key_input_ids, + query_token_type_ids=query_token_type_ids, + key_token_type_ids=key_token_type_ids, + query_attention_mask=query_attention_mask, + key_attention_mask=key_attention_mask) diff --git a/examples/text_matching/diffcse/run_diffcse.py b/examples/text_matching/diffcse/run_diffcse.py index 1af2f0686d40..a7f87e02d5f4 100644 --- a/examples/text_matching/diffcse/run_diffcse.py +++ b/examples/text_matching/diffcse/run_diffcse.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - import os import time import random @@ -34,7 +33,6 @@ from eval_metrics import eval_metrics from data import read_text_single, read_text_pair, convert_example, create_dataloader, word_repetition - # yapf: disable parser = argparse.ArgumentParser() parser.add_argument("--mode", choices=["train", "eval", "infer"], default="infer", help="Select which mode to run model, defaults to infer.") @@ -68,7 +66,8 @@ def do_infer(model, tokenizer, data_loader): - assert isinstance(model, Encoder), "please make sure that model is instance of Encoder." + assert isinstance( + model, Encoder), "please make sure that model is instance of Encoder." sims = [] model.eval() with paddle.no_grad(): @@ -81,7 +80,7 @@ def do_infer(model, tokenizer, data_loader): key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask, - ) + ) sims.append(cosine_sim.numpy()) sims = np.concatenate(sims, axis=0) model.train() @@ -89,7 +88,8 @@ def do_infer(model, tokenizer, data_loader): def do_eval(model, tokenizer, data_loader): - assert isinstance(model, Encoder), "please make sure that model is instance of Encoder." + assert isinstance( + model, Encoder), "please make sure that model is instance of Encoder." total_num = 0 sims, labels = [], [] model.eval() @@ -104,40 +104,40 @@ def do_eval(model, tokenizer, data_loader): key_token_type_ids=key_token_type_ids, query_attention_mask=query_attention_mask, key_attention_mask=key_attention_mask, - ) + ) sims.append(cosine_sim.numpy()) labels.append(label.numpy()) - + sims = np.concatenate(sims, axis=0) - labels = np.concatenate(labels, axis=0) + labels = np.concatenate(labels, axis=0) eval_res = eval_metrics(labels, sims) model.train() return eval_res, total_num def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): - num_training_steps = args.max_steps if args.max_steps > 0 else len(train_data_loader) * args.epochs + num_training_steps = args.max_steps if args.max_steps > 0 else len( + train_data_loader) * args.epochs - lr_scheduler = LinearDecayWithWarmup( - args.learning_rate, - num_training_steps, - args.warmup_proportion - ) + lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps, + args.warmup_proportion) - decay_params = [p.name for n, p in model.named_parameters() if not any(nd in n for nd in ["bias", "norm"])] + decay_params = [ + p.name for n, p in model.named_parameters() + if not any(nd in n for nd in ["bias", "norm"]) + ] optimizer = paddle.optimizer.AdamW( learning_rate=lr_scheduler, parameters=model.parameters(), weight_decay=args.weight_decay, - apply_decay_param_fun=lambda x: x in decay_params - ) + apply_decay_param_fun=lambda x: x in decay_params) global_step = 0 best_spearman = 0.0 best_pre_rec = 0.0 best_pre = 0.0 best_rec = 0.0 - best_rec95 = 0.0 + best_rec95 = 0.0 tic_train = time.time() model = paddle.DataParallel(model) model.train() @@ -145,116 +145,178 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): for step, batch in enumerate(train_data_loader, start=1): query_input_ids, query_token_type_ids, query_attention_mask, key_input_ids, key_token_type_ids, key_attention_mask = batch - loss, rtd_loss = model( - query_input_ids, - key_input_ids, - query_token_type_ids=query_token_type_ids, - key_token_type_ids=key_token_type_ids, - query_attention_mask=query_attention_mask, - key_attention_mask=key_attention_mask - ) + loss, rtd_loss = model(query_input_ids, + key_input_ids, + query_token_type_ids=query_token_type_ids, + key_token_type_ids=key_token_type_ids, + query_attention_mask=query_attention_mask, + key_attention_mask=key_attention_mask) global_step += 1 if global_step % (args.eval_steps // 10) == 0 and rank == 0: - print("global step {}, epoch: {}, batch: {}, loss: {:.5f}, rtd_loss: {:.5f}, rtd_acc: {:.5f}, rtd_rep_acc: {:.5f}, rtd_fix_acc: {:.5f}, speed: {:.2f} step/s".format(global_step, epoch, step, loss.item(), rtd_loss.item(), model._layers.rtd_acc, model._layers.rtd_rep_acc, model._layers.rtd_fix_acc, (args.eval_steps // 10) / (time.time() - tic_train))) - writer.add_scalar(tag="train/loss", step=global_step, value=loss.item()) - writer.add_scalar(tag="train/rtd_loss", step=global_step, value=rtd_loss.item()) - writer.add_scalar(tag="train/rtd_acc", step=global_step, value=model._layers.rtd_acc) - writer.add_scalar(tag="train/rtd_rep_acc", step=global_step, value=model._layers.rtd_rep_acc) - writer.add_scalar(tag="train/rtd_fix_acc", step=global_step, value=model._layers.rtd_fix_acc) + print( + "global step {}, epoch: {}, batch: {}, loss: {:.5f}, rtd_loss: {:.5f}, rtd_acc: {:.5f}, rtd_rep_acc: {:.5f}, rtd_fix_acc: {:.5f}, speed: {:.2f} step/s" + .format(global_step, epoch, step, loss.item(), + rtd_loss.item(), model._layers.rtd_acc, + model._layers.rtd_rep_acc, + model._layers.rtd_fix_acc, (args.eval_steps // 10) / + (time.time() - tic_train))) + writer.add_scalar(tag="train/loss", + step=global_step, + value=loss.item()) + writer.add_scalar(tag="train/rtd_loss", + step=global_step, + value=rtd_loss.item()) + writer.add_scalar(tag="train/rtd_acc", + step=global_step, + value=model._layers.rtd_acc) + writer.add_scalar(tag="train/rtd_rep_acc", + step=global_step, + value=model._layers.rtd_rep_acc) + writer.add_scalar(tag="train/rtd_fix_acc", + step=global_step, + value=model._layers.rtd_fix_acc) tic_train = time.time() if global_step % args.eval_steps == 0 and rank == 0: - eval_res, total_num = do_eval(model._layers.encoder, tokenizer, dev_data_loader) + eval_res, total_num = do_eval(model._layers.encoder, tokenizer, + dev_data_loader) spearman = eval_res["spearman_corr"] if best_spearman < spearman: - print("best checkpoint has been updated: from last best_spearman {} --> new spearman {}.".format(best_spearman, spearman)) + print( + "best checkpoint has been updated: from last best_spearman {} --> new spearman {}." + .format(best_spearman, spearman)) best_spearman = spearman # save best model save_dir = os.path.join(args.save_dir, "best_spearman") if not os.path.exists(save_dir): os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, "model_state.pdparams") - paddle.save(model._layers.encoder.state_dict(), save_param_path) + save_param_path = os.path.join(save_dir, + "model_state.pdparams") + paddle.save(model._layers.encoder.state_dict(), + save_param_path) tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: - f.write(str(global_step)+" "+str(epoch)+ " "+ str(step)) - - writer.add_scalar(tag="eval/spearman", step=global_step, value=spearman) - + with open(os.path.join(save_dir, "best_step.txt"), + "w", + encoding="utf-8") as f: + f.write( + str(global_step) + " " + str(epoch) + " " + + str(step)) + + writer.add_scalar(tag="eval/spearman", + step=global_step, + value=spearman) + pre_rec = eval_res["best_pre_rec_thr"][0] if best_pre_rec < pre_rec: - print("best checkpoint has been updated: from last best_pre_rec {} --> new pre_rec {}.".format(best_pre_rec, pre_rec)) + print( + "best checkpoint has been updated: from last best_pre_rec {} --> new pre_rec {}." + .format(best_pre_rec, pre_rec)) best_pre_rec = pre_rec # save best model save_dir = os.path.join(args.save_dir, "best_pre_rec") if not os.path.exists(save_dir): os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, "model_state.pdparams") - paddle.save(model._layers.encoder.state_dict(), save_param_path) + save_param_path = os.path.join(save_dir, + "model_state.pdparams") + paddle.save(model._layers.encoder.state_dict(), + save_param_path) tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: - f.write(str(global_step)+" "+str(epoch)+ " "+ str(step)) - - writer.add_scalar(tag="eval/pre_rec", step=global_step, value=pre_rec) - + with open(os.path.join(save_dir, "best_step.txt"), + "w", + encoding="utf-8") as f: + f.write( + str(global_step) + " " + str(epoch) + " " + + str(step)) + + writer.add_scalar(tag="eval/pre_rec", + step=global_step, + value=pre_rec) + pre = eval_res["best_pre_rec_thr"][1] if best_pre < pre: - print("best checkpoint has been updated: from last best_pre {} --> new pre {}.".format(best_pre, pre)) + print( + "best checkpoint has been updated: from last best_pre {} --> new pre {}." + .format(best_pre, pre)) best_pre = pre # save best model save_dir = os.path.join(args.save_dir, "best_pre") if not os.path.exists(save_dir): os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, "model_state.pdparams") - paddle.save(model._layers.encoder.state_dict(), save_param_path) + save_param_path = os.path.join(save_dir, + "model_state.pdparams") + paddle.save(model._layers.encoder.state_dict(), + save_param_path) tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: - f.write(str(global_step)+" "+str(epoch)+ " "+ str(step)) + with open(os.path.join(save_dir, "best_step.txt"), + "w", + encoding="utf-8") as f: + f.write( + str(global_step) + " " + str(epoch) + " " + + str(step)) writer.add_scalar(tag="eval/pre", step=global_step, value=pre) rec = eval_res["best_pre_rec_thr"][2] if best_rec < rec: - print("best checkpoint has been updated: from last best_rec {} --> new rec {}.".format(best_rec, rec)) + print( + "best checkpoint has been updated: from last best_rec {} --> new rec {}." + .format(best_rec, rec)) best_rec = rec # save best model save_dir = os.path.join(args.save_dir, "best_rec") if not os.path.exists(save_dir): os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, "model_state.pdparams") - paddle.save(model._layers.encoder.state_dict(), save_param_path) + save_param_path = os.path.join(save_dir, + "model_state.pdparams") + paddle.save(model._layers.encoder.state_dict(), + save_param_path) tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: - f.write(str(global_step)+" "+str(epoch)+ " "+ str(step)) + with open(os.path.join(save_dir, "best_step.txt"), + "w", + encoding="utf-8") as f: + f.write( + str(global_step) + " " + str(epoch) + " " + + str(step)) writer.add_scalar(tag="eval/rec", step=global_step, value=rec) rec95 = eval_res["best_pre_rec_thr_at_K"][1] if best_rec95 < rec95: - print("best checkpoint has been updated: from last best_rec95 {} --> new rec95 {}.".format(best_rec95, rec95)) + print( + "best checkpoint has been updated: from last best_rec95 {} --> new rec95 {}." + .format(best_rec95, rec95)) best_rec95 = rec95 # save best model save_dir = os.path.join(args.save_dir, "best_rec95") if not os.path.exists(save_dir): os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, "model_state.pdparams") - paddle.save(model._layers.encoder.state_dict(), save_param_path) + save_param_path = os.path.join(save_dir, + "model_state.pdparams") + paddle.save(model._layers.encoder.state_dict(), + save_param_path) tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), "w", encoding="utf-8") as f: - f.write(str(global_step)+" "+str(epoch)+ " "+ str(step)) - - writer.add_scalar(tag="eval/rec95", step=global_step, value=rec95) + with open(os.path.join(save_dir, "best_step.txt"), + "w", + encoding="utf-8") as f: + f.write( + str(global_step) + " " + str(epoch) + " " + + str(step)) + + writer.add_scalar(tag="eval/rec95", + step=global_step, + value=rec95) model.train() - + loss.backward() optimizer.step() lr_scheduler.step() optimizer.clear_grad() if global_step % args.save_steps == 0 and rank == 0: - save_dir = os.path.join(args.save_dir, "checkpoint_{}".format(global_step)) + save_dir = os.path.join(args.save_dir, + "checkpoint_{}".format(global_step)) if not os.path.exists(save_dir): os.makedirs(save_dir) save_param_path = os.path.join(save_dir, "model_state.pdparams") @@ -278,25 +340,37 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): os.makedirs(args.save_dir) # define tokenizer for processing data - tokenizer = ppnlp.transformers.AutoTokenizer.from_pretrained(args.encoder_name) - trans_func = partial( - convert_example, - tokenizer=tokenizer, - max_seq_length=args.max_seq_length - ) + tokenizer = ppnlp.transformers.AutoTokenizer.from_pretrained( + args.encoder_name) + trans_func = partial(convert_example, + tokenizer=tokenizer, + max_seq_length=args.max_seq_length) if args.mode == "train": start_time = time.time() # load data - train_ds = load_dataset(read_text_single, data_path=args.train_set_file, lazy=False) - dev_ds = load_dataset(read_text_pair, data_path=args.eval_set_file, lazy=False) - gen_tokenizer = ppnlp.transformers.AutoTokenizer.from_pretrained(args.generator_name) - dis_tokenizer = ppnlp.transformers.AutoTokenizer.from_pretrained(args.discriminator_name) + train_ds = load_dataset(read_text_single, + data_path=args.train_set_file, + lazy=False) + dev_ds = load_dataset(read_text_pair, + data_path=args.eval_set_file, + lazy=False) + gen_tokenizer = ppnlp.transformers.AutoTokenizer.from_pretrained( + args.generator_name) + dis_tokenizer = ppnlp.transformers.AutoTokenizer.from_pretrained( + args.discriminator_name) # intializing DiffCSE model - model = DiffCSE(encoder_name=args.encoder_name, generator_name=args.generator_name, discriminator_name=args.discriminator_name, gen_tokenizer=gen_tokenizer, dis_tokenizer=dis_tokenizer, - temp=args.temp, output_emb_size=args.output_emb_size, mlm_probability=args.mlm_probability, lambda_weight=args.lambda_weight) + model = DiffCSE(encoder_name=args.encoder_name, + generator_name=args.generator_name, + discriminator_name=args.discriminator_name, + gen_tokenizer=gen_tokenizer, + dis_tokenizer=dis_tokenizer, + temp=args.temp, + output_emb_size=args.output_emb_size, + mlm_probability=args.mlm_probability, + lambda_weight=args.lambda_weight) batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input @@ -316,41 +390,46 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): Stack(dtype="int64"), # labels ): [data for data in fn(samples)] - train_data_loader = create_dataloader( - train_ds, - mode="train", - batch_size=args.batch_size, - batchify_fn=batchify_fn, - trans_fn=trans_func - ) - dev_data_loader = create_dataloader( - dev_ds, - mode="eval", - batch_size=args.batch_size, - batchify_fn=dev_batchify_fn, - trans_fn=trans_func - ) + train_data_loader = create_dataloader(train_ds, + mode="train", + batch_size=args.batch_size, + batchify_fn=batchify_fn, + trans_fn=trans_func) + dev_data_loader = create_dataloader(dev_ds, + mode="eval", + batch_size=args.batch_size, + batchify_fn=dev_batchify_fn, + trans_fn=trans_func) with LogWriter(logdir=os.path.join(args.log_dir, "scalar")) as writer: - do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=writer) + do_train(model, + tokenizer, + train_data_loader, + dev_data_loader, + writer=writer) end_time = time.time() print("running time {} s".format(end_time - start_time)) - if args.mode == "eval": start_time = time.time() # initalizing encoder model for eval - model = Encoder(args.encoder_name, temp=args.temp, output_emb_size=args.output_emb_size) + model = Encoder(args.encoder_name, + temp=args.temp, + output_emb_size=args.output_emb_size) # load model from saved checkpoint if args.ckpt_dir: init_from_ckpt = os.path.join(args.ckpt_dir, "model_state.pdparams") if os.path.isfile(init_from_ckpt): - print("*************************initializing model from {}*****************************".format(init_from_ckpt)) + print( + "*************************initializing model from {}*****************************" + .format(init_from_ckpt)) state_dict = paddle.load(init_from_ckpt) model.set_dict(state_dict) - dev_ds = load_dataset(read_text_pair, data_path=args.eval_set_file, lazy=False) + dev_ds = load_dataset(read_text_pair, + data_path=args.eval_set_file, + lazy=False) dev_batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input @@ -362,33 +441,38 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): Stack(dtype="int64"), # labels ): [data for data in fn(samples)] - dev_data_loader = create_dataloader( - dev_ds, - mode="eval", - batch_size=args.batch_size, - batchify_fn=dev_batchify_fn, - trans_fn=trans_func - ) + dev_data_loader = create_dataloader(dev_ds, + mode="eval", + batch_size=args.batch_size, + batchify_fn=dev_batchify_fn, + trans_fn=trans_func) eval_res, total_num = do_eval(model, tokenizer, dev_data_loader) end_time = time.time() print("running time {} s".format(end_time - start_time)) - if args.mode == "infer": + if args.mode == "infer": start_time = time.time() # initalizing encoder model for eval - model = Encoder(args.encoder_name, temp=args.temp, output_emb_size=args.output_emb_size) + model = Encoder(args.encoder_name, + temp=args.temp, + output_emb_size=args.output_emb_size) # load model from saved checkpoint if args.ckpt_dir: init_from_ckpt = os.path.join(args.ckpt_dir, "model_state.pdparams") if os.path.isfile(init_from_ckpt): - print("*************************initializing model from {}*****************************".format(init_from_ckpt)) + print( + "*************************initializing model from {}*****************************" + .format(init_from_ckpt)) state_dict = paddle.load(init_from_ckpt) model.set_dict(state_dict) - infer_ds = load_dataset(read_text_pair, data_path=args.infer_set_file, lazy=False, is_infer=True) - + infer_ds = load_dataset(read_text_pair, + data_path=args.infer_set_file, + lazy=False, + is_infer=True) + batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # query_segment @@ -398,13 +482,11 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): Pad(axis=0, pad_val=0), # attention_mask ): [data for data in fn(samples)] - infer_data_loader = create_dataloader( - infer_ds, - mode="infer", - batch_size=args.batch_size, - batchify_fn=batchify_fn, - trans_fn=trans_func - ) + infer_data_loader = create_dataloader(infer_ds, + mode="infer", + batch_size=args.batch_size, + batchify_fn=batchify_fn, + trans_fn=trans_func) cosin_sim = do_infer(model, tokenizer, infer_data_loader) @@ -412,10 +494,8 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): for idx, cos in enumerate(cosin_sim): msg = "{} --> {}\n".format(idx, cos) f.write(msg) - print("Inference result has been saved to : {}".format(args.save_infer_path)) + print("Inference result has been saved to : {}".format( + args.save_infer_path)) end_time = time.time() print("running time {} s".format(end_time - start_time)) - - - diff --git a/examples/text_matching/diffcse/utils.py b/examples/text_matching/diffcse/utils.py index 8ef350bac183..6fa7dcb9fcdc 100644 --- a/examples/text_matching/diffcse/utils.py +++ b/examples/text_matching/diffcse/utils.py @@ -12,17 +12,18 @@ # See the License for the specific language governing permissions and # limitations under the License. - import random import numpy as np import paddle + def set_seed(seed=0): random.seed(seed) np.random.seed(seed) paddle.seed(seed) + def masked_fill(x, mask, value): y = paddle.full(x.shape, value, x.dtype) return paddle.where(mask, y, x) From 7bd988a9ce42e29d97da59a7e4a8d4ec5e72be21 Mon Sep 17 00:00:00 2001 From: milen <1649759610@qq.com> Date: Tue, 12 Jul 2022 12:51:19 +0000 Subject: [PATCH 009/116] initial commit --- examples/text_matching/diffcse/model.py | 17 +++++++++++++++-- examples/text_matching/diffcse/run_diffcse.py | 1 + 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/examples/text_matching/diffcse/model.py b/examples/text_matching/diffcse/model.py index 300b538bef8c..05844229ce8d 100644 --- a/examples/text_matching/diffcse/model.py +++ b/examples/text_matching/diffcse/model.py @@ -203,6 +203,7 @@ def __init__(self, encoder_name, generator_name, discriminator_name, + enc_tokenizer, gen_tokenizer, dis_tokenizer, temp=0.05, @@ -213,6 +214,7 @@ def __init__(self, self.encoder_name = encoder_name self.generator_name = generator_name self.discriminator_name = discriminator_name + self.enc_tokenizer = enc_tokenizer self.gen_tokenizer = gen_tokenizer self.dis_tokenizer = dis_tokenizer self.temp = temp @@ -250,10 +252,11 @@ def train_forward(self, with paddle.no_grad(): # mask tokens for query and key input_ids and then predict mask token with generator input_ids = paddle.concat([query_input_ids, key_input_ids], axis=0) + if self.encoder_name != self.generator_name: + input_ids = self.encode_by_generator(input_ids) attention_mask = paddle.concat( [query_attention_mask, key_attention_mask], axis=0) - mlm_input_ids, _ = mask_tokens(paddle.concat( - [query_input_ids, key_input_ids], axis=0), + mlm_input_ids, _ = mask_tokens(input_ids, self.gen_tokenizer, mlm_probability=self.mlm_probability) # predict tokens using generator @@ -284,6 +287,16 @@ def train_forward(self, return loss, rtd_loss + def encode_by_generator(self, batch_tokens): + new_tokens = [] + for one_tokens in batch_tokens: + one_gen_tokens = self.enc_tokenizer.convert_ids_to_tokens( + one_tokens.tolist()) + new_tokens.append( + self.gen_tokenizer.convert_tokens_to_ids(one_gen_tokens)) + + return paddle.to_tensor(new_tokens) + def encode_by_discriminator(self, batch_tokens): new_tokens = [] for one_tokens in batch_tokens: diff --git a/examples/text_matching/diffcse/run_diffcse.py b/examples/text_matching/diffcse/run_diffcse.py index a7f87e02d5f4..32db44b81e7f 100644 --- a/examples/text_matching/diffcse/run_diffcse.py +++ b/examples/text_matching/diffcse/run_diffcse.py @@ -365,6 +365,7 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): model = DiffCSE(encoder_name=args.encoder_name, generator_name=args.generator_name, discriminator_name=args.discriminator_name, + enc_tokenizer=tokenizer, gen_tokenizer=gen_tokenizer, dis_tokenizer=dis_tokenizer, temp=args.temp, From 02a997bcd78169ba2232efd2a4b462240a65de5d Mon Sep 17 00:00:00 2001 From: milen <1649759610@qq.com> Date: Thu, 28 Jul 2022 04:10:01 +0000 Subject: [PATCH 010/116] use common metric instead of eval_metrics.py and remove unuseful code --- examples/text_matching/diffcse/README.md | 73 +++++--- examples/text_matching/diffcse/data.py | 45 ----- .../text_matching/diffcse/eval_metrics.py | 174 ------------------ examples/text_matching/diffcse/model.py | 1 - examples/text_matching/diffcse/run_diffcse.py | 153 +++------------ examples/text_matching/diffcse/run_eval.sh | 6 +- examples/text_matching/diffcse/run_infer.sh | 6 +- examples/text_matching/diffcse/run_train.sh | 8 +- examples/text_matching/diffcse/utils.py | 6 + 9 files changed, 85 insertions(+), 387 deletions(-) delete mode 100644 examples/text_matching/diffcse/eval_metrics.py diff --git a/examples/text_matching/diffcse/README.md b/examples/text_matching/diffcse/README.md index 114026eeeb75..d08bd3bbe6d0 100644 --- a/examples/text_matching/diffcse/README.md +++ b/examples/text_matching/diffcse/README.md @@ -1,6 +1,6 @@ # 无监督语义匹配模型 [DiffCSE](https://arxiv.org/pdf/2204.10298.pdf) -我们借鉴 DiffCSE 论文的关键思想，实现了 DiffCSE 模型。相比于 SimCSE 模型，DiffCSE模型会更关注语句之间的差异性，具有精确的向量表示能力。DiffCSE 模型同样适合缺乏监督数据，但是又有大量无监督数据的匹配和检索场景。 +借鉴 [DiffCSE](https://arxiv.org/pdf/2204.10298.pdf) 的思路，实现了 DiffCSE 模型。相比于 SimCSE 模型，DiffCSE模型会更关注语句之间的差异性，具有精确的向量表示能力。DiffCSE 模型同样适合缺乏监督数据，但是又有大量无监督数据的匹配和检索场景。 ## 快速开始 ### 代码结构说明 @@ -10,10 +10,9 @@ ``` DiffCSE/ ├── model.py # DiffCSE 模型组网代码 -├── custom_ernie.py # 为适配DiffCSE模型，对ERNIE模型进行了部分修改 +├── custom_ernie.py # 为适配 DiffCSE 模型，对ERNIE模型进行了部分修改 ├── data.py # 无监督语义匹配训练数据、测试数据的读取逻辑 ├── run_diffcse.py # 模型训练、评估、预测的主脚本 -├── eval_metrics.py # 模型测试用的指标计算脚本，包括 spearman, Precison, Recall 等指标 ├── utils.py # 包括一些常用的工具式函数 ├── run_train.sh # 模型训练的脚本 ├── run_eval.sh # 模型评估的脚本 @@ -21,13 +20,21 @@ DiffCSE/ ``` ### 模型训练 +默认使用无监督模式进行训练 DiffCSE，模型训练数据的数据样例如下所示，每行表示一条训练样本： +```shell +全年地方财政总收入3686.81亿元，比上年增长12.3%。 +“我对案情并不十分清楚，所以没办法提出批评，建议，只能希望通过质询，要求检察院对此做出说明。”他说。 +据调查结果显示：2015年微商行业总体市场规模达到1819.5亿元，预计2016年将达到3607.3亿元，增长率为98.3%。 +前往冈仁波齐需要办理目的地包含日喀则和阿里地区的边防证，外转沿途有一些补给点，可购买到干粮和饮料。 +``` + 可以运行如下命令，开始模型训练并且进行模型测试。 ```shell gpu_ids=0 export CUDA_VISIBLE_DEVICES=${gpu_ids} -log_dir="log_diffcse_ErG3D3" +log_dir="log_train" python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ run_diffcse.py \ --mode "train" \ @@ -36,14 +43,14 @@ python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ --discriminator_name "ernie-3.0-base-zh" \ --max_seq_length "128" \ --output_emb_size "32" \ - --train_set_file "../dataset/100w/train_webdata.txt" \ - --eval_set_file "../dataset/100w/test_v1.txt" \ - --save_dir "./checkpoints_diffcse_ErG3D3" \ + --train_set_file "your train_set path" \ + --eval_set_file "your dev_set path" \ + --save_dir "./checkpoints" \ --log_dir ${log_dir} \ --save_steps "50000" \ --eval_steps "1000" \ - --batch_size "32" \ --epochs "3" \ + --batch_size "32" \ --mlm_probability "0.15" \ --lambda_weight "0.15" \ --learning_rate "3e-5" \ @@ -58,20 +65,21 @@ python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ * `encoder_name`：可选，DiffCSE模型中用于向量抽取的模型名称；默认为 ernie-3.0-base-zh。 * `generator_name`: 可选，DiffCSE模型中生成器的模型名称；默认为 ernie-3.0-base-zh。 * `discriminator_name`: 可选，DiffCSE模型中判别器的模型名称；默认为 rocketqa-zh-dureader-query-encoder。 -* `epochs`: 模型训练轮次，默认为3。 -* `batch_size`：可选，批处理大小，请结合显存情况进行调整，若出现显存不足，请适当调低这一参数；默认为32。 -* `learning_rate`：可选，Fine-tune 的最大学习率；默认为5e-5。 -* `weight_decay`：可选，控制正则项力度的参数，用于防止过拟合，默认为0.01。 -* `warmup_proportion`：可选，学习率 warmup 策略的比例，如果0.1，则学习率会在前10%训练step的过程中从0慢慢增长到 learning_rate, 而后再缓慢衰减，默认为0.01。 -* `mlm_probability`：可选，利用生成器预测时，控制单词掩码的比例，默认为0.15。 -* `lambda_weight`：可选，控制RTD任务loss的占比，默认为0.15。 * `max_seq_length`：可选，ERNIE-Gram 模型使用的最大序列长度，最大不能超过512, 若出现显存不足，请适当调低这一参数；默认为128。 * `output_emb_size`：可选，向量抽取模型输出向量的维度；默认为32。 * `train_set_file`：可选，用于指定训练集的路径。 * `eval_set_file`：可选，用于指定验证集的路径。 * `save_dir`：可选，保存训练模型的目录； +* `log_dir`：可选，训练训练过程中日志的输出目录； * `save_steps`：可选，用于指定模型训练过程中每隔多少 step 保存一次模型。 * `eval_steps`：可选，用于指定模型训练过程中每隔多少 step，使用验证集评估一次模型。 +* `epochs`: 模型训练轮次，默认为3。 +* `batch_size`：可选，批处理大小，请结合显存情况进行调整，若出现显存不足，请适当调低这一参数；默认为32。 +* `mlm_probability`：可选，利用生成器预测时，控制单词掩码的比例，默认为0.15。 +* `lambda_weight`：可选，控制RTD任务loss的占比，默认为0.15。 +* `learning_rate`：可选，Fine-tune 的最大学习率；默认为5e-5。 +* `weight_decay`：可选，控制正则项力度的参数，用于防止过拟合，默认为0.01。 +* `warmup_proportion`：可选，学习率 warmup 策略的比例，如果0.1，则学习率会在前10%训练step的过程中从0慢慢增长到 learning_rate, 而后再缓慢衰减，默认为0.01。 * `seed`：可选，随机种子，默认为1000. * `device`: 选用什么设备进行训练，可选 cpu 或 gpu。如使用 gpu 训练则参数 gpus 指定GPU卡号。 @@ -79,7 +87,7 @@ python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ 如： ```text checkpoints/ -├── model_best_spearman +├── best │   ├── model_state.pdparams │   ├── tokenizer_config.json │   ├── special_tokens_map.json @@ -88,6 +96,16 @@ checkpoints/ ``` ### 模型评估 +在模型评估时，需要使用带有标签的数据，以下展示了几条模型评估数据样例，每行表示一条训练样本，每行共计包含3列，分别是query1， query2， label： +```shell +右键单击此电脑选择属性，如下图所示 右键单击此电脑选择属性，如下图所示 5 +好医生解密||是什么，让美洲大蠊能美容还能救命 解密美洲大蠊巨大药用价值 1 +蒜香蜜汁烤鸡翅的做法 外香里嫩一口爆汁蒜蓉蜜汁烤鸡翅的做法 3 +项目计划书 篇2 简易项目计划书（参考模板） 2 +夏天幼儿园如何正确使用空调？ 老师们该如何正确使用空调，让孩子少生病呢？ 3 +``` + + 可以运行如下命令，进行模型评估。 ```shell @@ -101,12 +119,11 @@ python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ --encoder_name "rocketqa-zh-dureader-query-encoder" \ --max_seq_length "128" \ --output_emb_size "32" \ - --eval_set_file "../diffcse/data/test_v1.txt" \ - --ckpt_dir "./checkpoints_diffcse_ErG3D3/best_spearman" \ - --batch_size "8" \ + --eval_set_file "your dev_set path" \ + --ckpt_dir "./checkpoints/best" \ + --batch_size "32" \ --seed "0" \ --device "gpu" - ``` 可支持配置的参数： * `ckpt_dir`: 用于指定进行模型评估的checkpoint路径。 @@ -114,6 +131,12 @@ python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ 其他参数解释同上。 ### 基于动态图模型预测 +在模型预测时，需要给定待预测的两条文本，以下展示了几条模型预测的数据样例，每行表示一条训练样本，每行共计包含2列，分别是query1， query2： +```shell +韩国现代摩比斯2015招聘 韩国现代摩比斯2015校园招聘信息 +《DNF》封号减刑方法 被封一年怎么办? DNF封号减刑方法 封号一年怎么减刑 +原神手鞠游戏三个刷新位置一览 手鞠游戏三个刷新位置一览 +``` 可以运行如下命令，进行模型预测： ```shell @@ -127,21 +150,19 @@ python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ --encoder_name "rocketqa-zh-dureader-query-encoder" \ --max_seq_length "128" \ --output_emb_size "32" \ - --infer_set_file "../diffcse/data/test.txt" \ - --ckpt_dir "./checkpoints_diffcse_ErG3D3/best_spearman" \ + --infer_set_file "your test_set path \ + --ckpt_dir "./checkpoints/best" \ --save_infer_path "./infer_result.txt" \ - --batch_size "8" \ + --batch_size "32" \ --seed "0" \ --device "gpu" - - ``` 可支持配置的参数： * `infer_set_file`: 可选，用于指定测试集的路径。 * `save_infer_path`: 可选，用于保存模型预测结果的文件路径。 -其他参数解释同上。 +其他参数解释同上。 待模型预测结束后，会将结果保存至save_infer_path参数指定的文件中。 ## Reference diff --git a/examples/text_matching/diffcse/data.py b/examples/text_matching/diffcse/data.py index 5ecc7c02f73a..6b8cee1c1978 100644 --- a/examples/text_matching/diffcse/data.py +++ b/examples/text_matching/diffcse/data.py @@ -141,48 +141,3 @@ def read_text_pair(data_path, is_infer=False): if len(data[0]) == 0 or len(data[1]) == 0 or len(data[2]) == 0: continue yield {"text_a": data[0], "text_b": data[1], "label": data[2]} - - -def word_repetition(input_ids, token_type_ids, dup_rate=0.32): - """Word Reptition Strategy""" - input_ids = input_ids.numpy().tolist() - token_type_ids = token_type_ids.numpy().tolist() - - batch_size, seq_len = len(input_ids), len(input_ids[0]) - repetitied_input_ids = [] - repetitied_token_type_ids = [] - rep_seq_len = seq_len - for batch_id in range(batch_size): - cur_input_id = input_ids[batch_id] - actual_len = np.count_nonzero(cur_input_id) - dup_word_index = [] - # If sequence length is less than 5, skip it - if (actual_len > 5): - dup_len = random.randint(a=0, b=max(2, int(dup_rate * actual_len))) - # Skip cls and sep position - dup_word_index = random.sample(list(range(1, actual_len - 1)), - k=dup_len) - - r_input_id = [] - r_token_type_id = [] - for idx, word_id in enumerate(cur_input_id): - # Insert duplicate word - if idx in dup_word_index: - r_input_id.append(word_id) - r_token_type_id.append(token_type_ids[batch_id][idx]) - r_input_id.append(word_id) - r_token_type_id.append(token_type_ids[batch_id][idx]) - after_dup_len = len(r_input_id) - repetitied_input_ids.append(r_input_id) - repetitied_token_type_ids.append(r_token_type_id) - rep_seq_len = max(rep_seq_len, after_dup_len) - - # Padding the data to the same length - for batch_id in range(batch_size): - after_dup_len = len(repetitied_input_ids[batch_id]) - pad_len = rep_seq_len - after_dup_len - repetitied_input_ids[batch_id] += [0] * pad_len - repetitied_token_type_ids[batch_id] += [0] * pad_len - - return paddle.to_tensor(repetitied_input_ids), paddle.to_tensor( - repetitied_token_type_ids) diff --git a/examples/text_matching/diffcse/eval_metrics.py b/examples/text_matching/diffcse/eval_metrics.py deleted file mode 100644 index b4695a47c30d..000000000000 --- a/examples/text_matching/diffcse/eval_metrics.py +++ /dev/null @@ -1,174 +0,0 @@ -# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import numpy as np -from scipy import stats -from sklearn import metrics - - -def eval_metrics(labels, sims): - - eval_res = {} - spearman_corr = stats.spearmanr(labels, sims).correlation - eval_res["spearman_corr"] = spearman_corr - print("Spearman corr --> {:.5f}\n".format(spearman_corr)) - - labels_2_cls = np.where(np.greater_equal(labels, 3), np.ones_like(labels), - np.zeros_like(labels)) - - pre, rec, thr = metrics.precision_recall_curve(labels_2_cls, - sims, - pos_label=1) - pre_rec = np.sum([pre, rec], axis=0) - best_pre_rec = np.max(pre_rec) - best_pre = pre[np.argmax(pre_rec)] - best_rec = rec[np.argmax(pre_rec)] - best_thr = thr[np.argmax(pre_rec)] - eval_res["best_pre_rec_thr"] = [best_pre_rec, best_pre, best_rec, best_thr] - print( - "2-CLS best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}" - .format(best_pre_rec, best_pre, best_rec, best_thr)) - - pre_rec_thr = list(zip(pre, rec, thr)) - pre_rec_thr.sort(key=lambda x: x[1], reverse=True) - pre_rec_thr_at_K = None - at_K = 0.95 - for x in pre_rec_thr: - if x[0] >= at_K: - pre_rec_thr_at_K = x - break - eval_res["best_pre_rec_thr_at_K"] = pre_rec_thr_at_K - print( - "2-CLS best pre@{:.2f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}". - format(at_K, pre_rec_thr_at_K[0], pre_rec_thr_at_K[1], - pre_rec_thr_at_K[2])) - - preds_2_cls = np.where(np.greater_equal(sims, best_thr), np.ones_like(sims), - np.zeros_like(sims)) - - acc_2_cls = metrics.accuracy_score(labels_2_cls, preds_2_cls) - auc_2_cls = metrics.roc_auc_score(labels_2_cls, sims) - report_2_cls = metrics.classification_report(labels_2_cls, - preds_2_cls, - labels=[0, 1], - target_names=["0", "1"], - digits=5, - output_dict=True) - eval_res["acc_2_cls"] = acc_2_cls - eval_res["auc_2_cls"] = auc_2_cls - eval_res["report_2_cls"] = report_2_cls - print("2-CLS metrics are evaluated on the best thr --> {}".format(best_thr)) - print("2-CLS accuracy --> {}".format(acc_2_cls)) - print("2-CLS roc-auc --> {}".format(auc_2_cls)) - print("\t".join(["label", "precision", "recall", "f1-score", "support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( - "2-CLS 0", report_2_cls["0"]["precision"], report_2_cls["0"]["recall"], - report_2_cls["0"]["f1-score"], report_2_cls["0"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}\n".format( - "2-CLS 1", report_2_cls["1"]["precision"], report_2_cls["1"]["recall"], - report_2_cls["1"]["f1-score"], report_2_cls["1"]["support"])) - - best_thr_6_cls = [] - for pivot in [5, 4, 3, 2, 1]: - labels_pivot = np.where(np.greater_equal(labels, pivot), - np.ones_like(labels), np.zeros_like(labels)) - pre_pivot, rec_pivot, thr_pivot = metrics.precision_recall_curve( - labels_pivot, sims, pos_label=1) - pre_rec_pivot = np.sum([pre_pivot, rec_pivot], axis=0) - best_pre_rec_pivot = np.max(pre_rec_pivot) - best_pre_pivot = pre_pivot[np.argmax(pre_rec_pivot)] - best_rec_pivot = rec_pivot[np.argmax(pre_rec_pivot)] - best_thr_pivot = thr_pivot[np.argmax(pre_rec_pivot)] - best_thr_6_cls.append(best_thr_pivot) - eval_res["best_pre_rec_thr_6_cls_at_{}_pivot".format(pivot)] = [ - best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, best_thr_pivot - ] - print( - "6-CLS [split at {} pivot] best pre+rec={:.5f}, pre --> {:.5f}, rec --> {:.5f}, thr --> {:.5f}" - .format(pivot, best_pre_rec_pivot, best_pre_pivot, best_rec_pivot, - best_thr_pivot)) - - preds = [] - for s in sims: - s = max(s, 0) - for pivot, thr in list(zip([5, 4, 3, 2, 1, 0], best_thr_6_cls + [0.0])): - if s + 1e-8 >= thr: - preds.append(pivot) - break - - acc_6_cls = metrics.accuracy_score(labels, preds) - - report_6_cls = metrics.classification_report( - labels, - preds, - labels=[0, 1, 2, 3, 4, 5], - target_names=["0", "1", "2", "3", "4", "5"], - digits=5, - output_dict=True) - eval_res["acc_6_cls"] = acc_6_cls - eval_res["report_6_cls"] = report_6_cls - print("6-CLS metrics are evaluated on the best thr --> {}".format( - best_thr_6_cls)) - print("6-CLS accuracy --> {}".format(acc_6_cls)) - print("\t".join(["label", "precision", "recall", "f1-score", "support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( - "6-CLS 0", report_6_cls["0"]["precision"], report_6_cls["0"]["recall"], - report_6_cls["0"]["f1-score"], report_6_cls["0"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( - "6-CLS 1", report_6_cls["1"]["precision"], report_6_cls["1"]["recall"], - report_6_cls["1"]["f1-score"], report_6_cls["1"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( - "6-CLS 2", report_6_cls["2"]["precision"], report_6_cls["2"]["recall"], - report_6_cls["2"]["f1-score"], report_6_cls["2"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( - "6-CLS 3", report_6_cls["3"]["precision"], report_6_cls["3"]["recall"], - report_6_cls["3"]["f1-score"], report_6_cls["3"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( - "6-CLS 4", report_6_cls["4"]["precision"], report_6_cls["4"]["recall"], - report_6_cls["4"]["f1-score"], report_6_cls["4"]["support"])) - print("{}\t{:.5f}\t{:.5f}\t{:.5f}\t{}".format( - "6-CLS 5", report_6_cls["5"]["precision"], report_6_cls["5"]["recall"], - report_6_cls["5"]["f1-score"], report_6_cls["5"]["support"])) - - return eval_res - - -def read_infer_result(infer_result): - sims = [] - with open(infer_result, 'r') as f: - for line in f: - line = line.strip() - if " --> " in line: - sim = float(line.split(' --> ')[1]) - sims.append(sim) - return sims - - -def read_infer_label(infer_label): - labels = [] - with open(infer_label, 'r') as f: - for line in f: - line = line.strip() - label = int(line.split('\t')[2]) - labels.append(label) - return labels - - -if __name__ == "__main__": - """ - example - """ - labels = read_infer_label("./data/test_v1.txt") - sims = read_infer_result("infer_res.txt") - eval_metrics(labels, sims) diff --git a/examples/text_matching/diffcse/model.py b/examples/text_matching/diffcse/model.py index 05844229ce8d..7739757d5350 100644 --- a/examples/text_matching/diffcse/model.py +++ b/examples/text_matching/diffcse/model.py @@ -137,7 +137,6 @@ def cosine_sim(self, cosine_sim = self.sim(query_cls_embedding, key_cls_embedding, one_vs_one=True) - # cosine_sim = paddle.sum(query_cls_embedding * key_cls_embedding, axis=-1) return cosine_sim def forward(self, diff --git a/examples/text_matching/diffcse/run_diffcse.py b/examples/text_matching/diffcse/run_diffcse.py index 32db44b81e7f..1d69a717ac26 100644 --- a/examples/text_matching/diffcse/run_diffcse.py +++ b/examples/text_matching/diffcse/run_diffcse.py @@ -29,9 +29,8 @@ from visualdl import LogWriter from model import DiffCSE, Encoder -from utils import set_seed -from eval_metrics import eval_metrics -from data import read_text_single, read_text_pair, convert_example, create_dataloader, word_repetition +from utils import set_seed, eval_metric +from data import read_text_single, read_text_pair, convert_example, create_dataloader # yapf: disable parser = argparse.ArgumentParser() @@ -90,13 +89,11 @@ def do_infer(model, tokenizer, data_loader): def do_eval(model, tokenizer, data_loader): assert isinstance( model, Encoder), "please make sure that model is instance of Encoder." - total_num = 0 sims, labels = [], [] model.eval() with paddle.no_grad(): for batch in data_loader: query_input_ids, query_token_type_ids, query_attention_mask, key_input_ids, key_token_type_ids, key_attention_mask, label = batch - total_num += len(label) cosine_sim = model.cosine_sim( query_input_ids=query_input_ids, key_input_ids=key_input_ids, @@ -110,9 +107,9 @@ def do_eval(model, tokenizer, data_loader): sims = np.concatenate(sims, axis=0) labels = np.concatenate(labels, axis=0) - eval_res = eval_metrics(labels, sims) + score = eval_metric(labels, sims) model.train() - return eval_res, total_num + return score def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): @@ -133,11 +130,7 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): apply_decay_param_fun=lambda x: x in decay_params) global_step = 0 - best_spearman = 0.0 - best_pre_rec = 0.0 - best_pre = 0.0 - best_rec = 0.0 - best_rec95 = 0.0 + best_score = 0. tic_train = time.time() model = paddle.DataParallel(model) model.train() @@ -155,11 +148,14 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): global_step += 1 if global_step % (args.eval_steps // 10) == 0 and rank == 0: print( - "global step {}, epoch: {}, batch: {}, loss: {:.5f}, rtd_loss: {:.5f}, rtd_acc: {:.5f}, rtd_rep_acc: {:.5f}, rtd_fix_acc: {:.5f}, speed: {:.2f} step/s" + "global step {}, epoch: {}, batch: {}, loss: {:.5f}, rtd_loss: {:.5f}, rtd_acc: {:.5f}, rtd_rep_acc: {:.5f}, rtd_fix_acc: {:.5f}, pos_avg: {:.5f}, neg_avg: {:.5f}, speed: {:.2f} step/s" .format(global_step, epoch, step, loss.item(), rtd_loss.item(), model._layers.rtd_acc, model._layers.rtd_rep_acc, - model._layers.rtd_fix_acc, (args.eval_steps // 10) / + model._layers.rtd_fix_acc, + model._layers.encoder.sim.pos_avg, + model._layers.encoder.sim.neg_avg, + (args.eval_steps // 10) / (time.time() - tic_train))) writer.add_scalar(tag="train/loss", step=global_step, @@ -180,17 +176,17 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): tic_train = time.time() if global_step % args.eval_steps == 0 and rank == 0: - eval_res, total_num = do_eval(model._layers.encoder, tokenizer, - dev_data_loader) - spearman = eval_res["spearman_corr"] + score = do_eval(model._layers.encoder, tokenizer, + dev_data_loader) + print("Evaluation - score:{:.5f}".format(score)) - if best_spearman < spearman: + if best_score < score: print( - "best checkpoint has been updated: from last best_spearman {} --> new spearman {}." - .format(best_spearman, spearman)) - best_spearman = spearman + "best checkpoint has been updated: from last best_score {} --> new score {}." + .format(best_score, score)) + best_score = score # save best model - save_dir = os.path.join(args.save_dir, "best_spearman") + save_dir = os.path.join(args.save_dir, "best") if not os.path.exists(save_dir): os.makedirs(save_dir) save_param_path = os.path.join(save_dir, @@ -198,116 +194,10 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): paddle.save(model._layers.encoder.state_dict(), save_param_path) tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), - "w", - encoding="utf-8") as f: - f.write( - str(global_step) + " " + str(epoch) + " " + - str(step)) - - writer.add_scalar(tag="eval/spearman", - step=global_step, - value=spearman) - - pre_rec = eval_res["best_pre_rec_thr"][0] - if best_pre_rec < pre_rec: - print( - "best checkpoint has been updated: from last best_pre_rec {} --> new pre_rec {}." - .format(best_pre_rec, pre_rec)) - best_pre_rec = pre_rec - # save best model - save_dir = os.path.join(args.save_dir, "best_pre_rec") - if not os.path.exists(save_dir): - os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, - "model_state.pdparams") - paddle.save(model._layers.encoder.state_dict(), - save_param_path) - tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), - "w", - encoding="utf-8") as f: - f.write( - str(global_step) + " " + str(epoch) + " " + - str(step)) - - writer.add_scalar(tag="eval/pre_rec", - step=global_step, - value=pre_rec) - - pre = eval_res["best_pre_rec_thr"][1] - if best_pre < pre: - print( - "best checkpoint has been updated: from last best_pre {} --> new pre {}." - .format(best_pre, pre)) - best_pre = pre - # save best model - save_dir = os.path.join(args.save_dir, "best_pre") - if not os.path.exists(save_dir): - os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, - "model_state.pdparams") - paddle.save(model._layers.encoder.state_dict(), - save_param_path) - tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), - "w", - encoding="utf-8") as f: - f.write( - str(global_step) + " " + str(epoch) + " " + - str(step)) - writer.add_scalar(tag="eval/pre", step=global_step, value=pre) - - rec = eval_res["best_pre_rec_thr"][2] - if best_rec < rec: - print( - "best checkpoint has been updated: from last best_rec {} --> new rec {}." - .format(best_rec, rec)) - best_rec = rec - # save best model - save_dir = os.path.join(args.save_dir, "best_rec") - if not os.path.exists(save_dir): - os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, - "model_state.pdparams") - paddle.save(model._layers.encoder.state_dict(), - save_param_path) - tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), - "w", - encoding="utf-8") as f: - f.write( - str(global_step) + " " + str(epoch) + " " + - str(step)) - - writer.add_scalar(tag="eval/rec", step=global_step, value=rec) - - rec95 = eval_res["best_pre_rec_thr_at_K"][1] - if best_rec95 < rec95: - print( - "best checkpoint has been updated: from last best_rec95 {} --> new rec95 {}." - .format(best_rec95, rec95)) - best_rec95 = rec95 - # save best model - save_dir = os.path.join(args.save_dir, "best_rec95") - if not os.path.exists(save_dir): - os.makedirs(save_dir) - save_param_path = os.path.join(save_dir, - "model_state.pdparams") - paddle.save(model._layers.encoder.state_dict(), - save_param_path) - tokenizer.save_pretrained(save_dir) - with open(os.path.join(save_dir, "best_step.txt"), - "w", - encoding="utf-8") as f: - f.write( - str(global_step) + " " + str(epoch) + " " + - str(step)) - - writer.add_scalar(tag="eval/rec95", + writer.add_scalar(tag="eval/score", step=global_step, - value=rec95) + value=score) model.train() loss.backward() @@ -448,7 +338,8 @@ def do_train(model, tokenizer, train_data_loader, dev_data_loader, writer=None): batchify_fn=dev_batchify_fn, trans_fn=trans_func) - eval_res, total_num = do_eval(model, tokenizer, dev_data_loader) + score = do_eval(model, tokenizer, dev_data_loader) + print("Evaluation - score:{:.5f}".format(score)) end_time = time.time() print("running time {} s".format(end_time - start_time)) diff --git a/examples/text_matching/diffcse/run_eval.sh b/examples/text_matching/diffcse/run_eval.sh index 9672cf37ef0f..0c5512c2f4d3 100644 --- a/examples/text_matching/diffcse/run_eval.sh +++ b/examples/text_matching/diffcse/run_eval.sh @@ -8,8 +8,8 @@ python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ --encoder_name "rocketqa-zh-dureader-query-encoder" \ --max_seq_length "128" \ --output_emb_size "32" \ - --eval_set_file "../diffcse/data/test_v1.txt" \ - --ckpt_dir "./checkpoints_diffcse_ErG3D3/best_spearman" \ - --batch_size "8" \ + --eval_set_file "your dev_set path" \ + --ckpt_dir "./checkpoints/best" \ + --batch_size "32" \ --seed "0" \ --device "gpu" diff --git a/examples/text_matching/diffcse/run_infer.sh b/examples/text_matching/diffcse/run_infer.sh index db808114e370..7df8a573cd8a 100644 --- a/examples/text_matching/diffcse/run_infer.sh +++ b/examples/text_matching/diffcse/run_infer.sh @@ -8,9 +8,9 @@ python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ --encoder_name "rocketqa-zh-dureader-query-encoder" \ --max_seq_length "128" \ --output_emb_size "32" \ - --infer_set_file "../diffcse/data/test.txt" \ - --ckpt_dir "./checkpoints_diffcse_ErG3D3/best_spearman" \ + --infer_set_file "your test_set path \ + --ckpt_dir "./checkpoints/best" \ --save_infer_path "./infer_result.txt" \ - --batch_size "8" \ + --batch_size "32" \ --seed "0" \ --device "gpu" diff --git a/examples/text_matching/diffcse/run_train.sh b/examples/text_matching/diffcse/run_train.sh index 9d6fd14e299e..9ed49a4b37e8 100644 --- a/examples/text_matching/diffcse/run_train.sh +++ b/examples/text_matching/diffcse/run_train.sh @@ -1,7 +1,7 @@ gpu_ids=0 export CUDA_VISIBLE_DEVICES=${gpu_ids} -log_dir="log_diffcse_ErG3D3" +log_dir="log_train" python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ run_diffcse.py \ --mode "train" \ @@ -10,9 +10,9 @@ python -u -m paddle.distributed.launch --gpus ${gpu_ids} --log_dir ${log_dir} \ --discriminator_name "ernie-3.0-base-zh" \ --max_seq_length "128" \ --output_emb_size "32" \ - --train_set_file "../dataset/100w/train_webdata.txt" \ - --eval_set_file "../dataset/100w/test_v1.txt" \ - --save_dir "./checkpoints_diffcse_ErG3D3" \ + --train_set_file "your train_set path" \ + --eval_set_file "your dev_set path" \ + --save_dir "./checkpoints" \ --log_dir ${log_dir} \ --save_steps "50000" \ --eval_steps "1000" \ diff --git a/examples/text_matching/diffcse/utils.py b/examples/text_matching/diffcse/utils.py index 6fa7dcb9fcdc..d70b434f8286 100644 --- a/examples/text_matching/diffcse/utils.py +++ b/examples/text_matching/diffcse/utils.py @@ -16,6 +16,7 @@ import numpy as np import paddle +from scipy import stats def set_seed(seed=0): @@ -27,3 +28,8 @@ def set_seed(seed=0): def masked_fill(x, mask, value): y = paddle.full(x.shape, value, x.dtype) return paddle.where(mask, y, x) + + +def eval_metric(labels, preds): + spearman_corr = stats.spearmanr(labels, preds).correlation + return spearman_corr From b837b676937b4226097bad4e1f1788efef302ed3 Mon Sep 17 00:00:00 2001 From: milen <1649759610@qq.com> Date: Mon, 7 Nov 2022 10:38:51 +0000 Subject: [PATCH 011/116] mv stage project to ASO_analysis --- .../ASO_analysis/.gitignore | 2 + .../sentiment_analysis/ASO_analysis/README.md | 200 ++++++++++++++ .../ASO_analysis/classification/README.md | 64 +++++ .../ASO_analysis/classification/data.py | 58 ++++ .../ASO_analysis/classification/evaluate.py | 80 ++++++ .../classification/run_evaluate.sh | 8 + .../ASO_analysis/classification/run_train.sh | 18 ++ .../ASO_analysis/classification/train.py | 161 +++++++++++ .../sentiment_analysis/ASO_analysis/demo.py | 136 ++++++++++ .../ASO_analysis/deploy/predict.py | 252 ++++++++++++++++++ .../ASO_analysis/deploy/run_predict.sh | 13 + .../ASO_analysis/doccano.py | 166 ++++++++++++ .../ASO_analysis/export_model.py | 55 ++++ .../ASO_analysis/extraction/README.md | 65 +++++ .../ASO_analysis/extraction/data.py | 65 +++++ .../ASO_analysis/extraction/evaluate.py | 85 ++++++ .../ASO_analysis/extraction/run_evaluate.sh | 9 + .../ASO_analysis/extraction/run_train.sh | 18 ++ .../ASO_analysis/extraction/train.py | 164 ++++++++++++ .../ASO_analysis/imgs/design_cls_model.png | Bin 0 -> 738007 bytes .../ASO_analysis/imgs/design_ext_model.png | Bin 0 -> 903156 bytes .../ASO_analysis/imgs/labeling_example.png | Bin 0 -> 146986 bytes .../ASO_analysis/imgs/sentiment_system.png | Bin 0 -> 349472 bytes .../ASO_analysis/pp_minilm/README.md | 136 ++++++++++ .../ASO_analysis/pp_minilm/data.py | 58 ++++ .../ASO_analysis/pp_minilm/evaluate.py | 79 ++++++ .../pp_minilm/performance_test.py | 198 ++++++++++++++ .../ASO_analysis/pp_minilm/quant_post.py | 97 +++++++ .../ASO_analysis/pp_minilm/run_evaluate.sh | 10 + .../pp_minilm/run_performance_test.sh | 10 + .../ASO_analysis/pp_minilm/run_quant.sh | 16 ++ .../ASO_analysis/pp_minilm/run_train.sh | 19 ++ .../ASO_analysis/pp_minilm/train.py | 160 +++++++++++ .../ASO_analysis/predict.py | 208 +++++++++++++++ .../ASO_analysis/run_demo.sh | 10 + .../ASO_analysis/run_export_model.sh | 27 ++ .../ASO_analysis/run_predict.sh | 13 + .../sentiment_analysis/ASO_analysis/utils.py | 133 +++++++++ 38 files changed, 2793 insertions(+) create mode 100644 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applications/sentiment_analysis/ASO_analysis/run_demo.sh create mode 100644 applications/sentiment_analysis/ASO_analysis/run_export_model.sh create mode 100644 applications/sentiment_analysis/ASO_analysis/run_predict.sh create mode 100644 applications/sentiment_analysis/ASO_analysis/utils.py diff --git a/applications/sentiment_analysis/ASO_analysis/.gitignore b/applications/sentiment_analysis/ASO_analysis/.gitignore new file mode 100644 index 000000000000..7d7a840f05d7 --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/.gitignore @@ -0,0 +1,2 @@ +checkpoints/* +data/* diff --git a/applications/sentiment_analysis/ASO_analysis/README.md b/applications/sentiment_analysis/ASO_analysis/README.md new file mode 100644 index 000000000000..25469f0a93e0 --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/README.md @@ -0,0 +1,200 @@ +# 评论观点抽取与情感倾向性分析 + +## 1. 场景概述 + +情感分析旨在对带有情感色彩的主观性文本进行分析、处理、归纳和推理，其广泛应用于消费决策、舆情分析、个性化推荐等领域，具有很高的商业价值。 + +依托百度领先的情感分析技术，食行生鲜自动生成菜品评论标签辅助用户购买，并指导运营采购部门调整选品和促销策略；房天下向购房者和开发商直观展示楼盘的用户口碑情况，并对好评楼盘置顶推荐；国美搭建服务智能化评分系统，客服运营成本减少40%，负面反馈处理率100%。 + +情感分析相关的任务有语句级情感分析、评论对象抽取、观点抽取等等。一般来讲，被人们所熟知的情感分析任务是语句级别的情感分析，该任务是在宏观上去分析整句话的感情色彩，其粒度可能相对比较粗。 + +因为在人们进行评论的时候，往往针对某一产品或服务进行多个属性的评论，对每个属性的评论可能也会褒贬不一，因此针对属性级别的情感分析在真实的场景中会更加实用，同时更能给到企业用户或商家更加具体的建议。例如这句关于薯片的评论。 + +> 这个薯片味道真的太好了，口感很脆，只是包装很一般。 + +可以看到，顾客在口感、包装和味道 三个属性上对薯片进行了评价，顾客在味道和口感两个方面给出了好评，但是在包装上给出了负面的评价。只有通过这种比较细粒度的分析，商家才能更有针对性的发现问题，进而改进自己的产品或服务。 + +基于这样的考虑，本项目提出了一种细粒度的情感分析能力，对于给定的文本，首先会抽取该文本中的评论观点，然后分析不同观点的情感极性。 + + +## 2. 产品功能介绍 + +### 2.1 系统特色 +为了降低技术门槛，方便开发者共享效果领先的情感分析技术，PaddleNLP本次开源的情感分析系统，具备三大亮点： + +- 覆盖任务全 + - 集成评论观点抽取、属性级情感分类等情感分析能力，并开源模型，且打通模型训练、评估、预测部署全流程。 +- 效果领先 + - 集成百度研发的基于情感知识增强的预训练模型SKEP，为各类情感分析任务提供统一且强大的情感语义表示能力。 +- 预测性能强 + - 针对预训练模型预测效率低的问题，开源小模型PP-MiniLM，量化优化策略，预测性能大幅提升。 + +### 2.2 架构&功能 + +本项目提出的的情感分析解决方案如图1所示，整个情感分析的过程大致包含两个阶段，依次是评论观点抽取模型，属性级情感分类模型。对于给定的一段文本，首先基于前者抽取出文本语句中潜在的评论属性以及该属性相应的评论观点，然后将评论属性、观点以及原始文本进行拼接，传给属性级情感分类模型以识别出该评论属性的情感极性。 + +这里需要提到的是，由于目前市面上的大多数模型是基于通用语料训练出来的，这些模型可能并不会对情感信息那么敏感。基于这样的考量，本项目使用了百度自研的 SKEP 预训练模型，其在预训练阶段便设计了多种情感信息相关的预训练目标进行训练。作为一种情感专属的模型，其更适合用来做上边提到的评论观点抽取任务，以及属性级情感分类任务。 + +另外，本项目使用的是 Large 版的 SKEP 模型，考虑到企业用户在线上部署时会考虑到模型预测效率，所以本项目专门提供了一个通用版的小模型 [PP-MiniLM](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/model_compression/pp-minilm) 以及一套量化策略，用户可以使用相应情感数据集对 PP-MiniLM 进行微调，然后进行量化，以达到更快的预测效率。 + +
+ +

图1 情感分析系统图

+

+ + +## 3. 情感分析效果展示 +在图1中可以看到，本项目的核心模块为评论观点抽取和属性级情感分类模块，本项目中基于情感专属模型 SKEP 实现了两个模块，并且提供了两者训练和测试的脚本，分别放在 `extraction` 和 `classification` 目录下。 + +下表展示了我们训练的评论观点抽取模型在验证集 `dev` 和测试集 `test` 上的表现： +|Model|数据集|precision|Recall|F1| +| ------------ | ------------ | ------------ |-----------|------------ | +|SKEP-Large|dev|0.87095|0.90056|0.88551| +|SKEP-Large|test|0.87125|0.89944|0.88512| + +下表展示了我们训练的属性级情感分类模型在验证集 `dev` 和测试集 `test` 上的表现： +|Model|数据集|precision|Recall|F1| +| ------------ | ------------ | ------------ |-----------|------------ | +|SKEP-Large|dev|0.98758|0.99251|0.99004| +|SKEP-Large|test|0.98497|0.99139|0.98817| + +给定一段文本，使用我们提供的全流程预测脚本可以轻松获得情感分析结果，如下所示。 + +- input_text: 蛋糕味道不错，很好吃，店家很耐心，服务也很好，很棒 + - aspect: 蛋糕味道, opinions: ['不错', '好吃'], sentiment_polarity: 正向 + - aspect: 店家, opinions: ['耐心'], sentiment_polarity: 正向 + - aspect: 服务, opinions: ['好', '棒'], sentiment_polarity: 正向 + +如果你想了解更多评论观点抽取模型和属性级情感分类模型的实现细节，请分别点击 [extraction](extraction/README.md) 和 [classification](classification/README.md)。 + + +## 4. 情感分析实践 +以下是本项目运行的完整目录结构以及说明： + +``` +. +├── extraction # 评价观点抽取模型包 +├── classification # 细粒度情感分类模型包 +├── pp_minilm # PP-MiniLM特色小模型包 +├── deploy # 高性能预测部署包 +│ ├── predict.py # 高性能预测脚本 +│   ├── run_predict.py # 高性能预测命令 +├── imgs # 图片目录 +├── demo.py # demo脚本,方便体验预测效果 +├── predict.py # 全流程预测脚本 +├── export_model.py # 动转静模型导出脚本 +├── utils.py # 工具函数脚本 +├── run_demo.sh # 运行demo，快速体验情感分析效果 +├── run_predict.sh # 全流程预测命令 +├── run_export_model.sh # 动转静模型导出命令 +└── README.md +``` + +### 4.1 运行环境和依赖安装 +(1) 环境依赖 + +- python >= 3.6 +- paddlenlp >= 2.2.2 +- paddlepaddle-gpu >= 2.2.1 + +(2) 运行环境准备 +在运行之前，请在本目录下新建目录 `data` 和 `checkpoints`，分别用于存放数据和保存模型。 + +本项目需要训练两个阶段的模型：评论观点抽取模型，属性级情感分类模型。本次针对这抽取和分类模型，我们分别开源了 Demo 数据： [ext_data](https://bj.bcebos.com/v1/paddlenlp/data/ext_data.tar.gz)和[cls_data](https://bj.bcebos.com/v1/paddlenlp/data/cls_data.tar.gz)。 + +用户可分别点击下载，解压后将相应的数据文件依次放入 `./data/ext_data` 和 `./data/cls_data` 目录下即可。 + +### 4.2 使用说明 +本项目开源了训练后的评论观点模型 [ext_model](https://bj.bcebos.com/paddlenlp/models/best_ext.pdparams) 和 属性级情感分类模型 [cls_model](https://bj.bcebos.com/paddlenlp/models/best_cls.pdparams)。如有需要，可点击下载，下载后请将 `ext_model` 和 `cls_model` 重命名为 `best.pdparams`，分别放入 `./checkpoints/ext_checkpoints` 和 `./checkpoints/cls_checkpoints` 中。 + +另外，考虑到不同用户可能有不同的需求，本项目提供了如下的方式学习或使用本项目。 + +**(1）快速体验效果** +如果你想快速体验本项目提供的情感分析能力，可使用本项目提供的 `demo.sh` 脚本以交互式的方式进行体验。 +```shell +sh run_demo.sh +``` + +**备注**：体验之前，请确保下载以上提到的 `ext_model` 和 `cls_model`，重命名后放入相应的目录中。 + +**(2) 文本批量预测** +如果你有一批数据，不方便逐句输入，可使用本项目提供的正式预测脚本 `predict.py`， 以文件的形式进行输入，处理后该脚本会将结果文件保存到与输入文件相同的目录下，默认的结果文件名为 `sentiment_results.json`。 + +本功能在预测时需要传入测试集文件路径，可将测试集文件命名为`test.txt`， 然后放入 `./data` 目录下。需要注意的是，测试集文件每行均为一个待预测的语句，如下所示。 + +- 蛋糕味道不错，很好吃，店家很耐心，服务也很好，很棒 +- 酒店干净整洁，性价比很高 +- 酒店环境不错，非常安静，性价比还可以 +- 房间很大，环境不错 + +通过运行如下命令，便可进行批量文本情感分析预测： +```shell +sh run_predict.sh +``` + +**备注**：体验之前，请确保下载以上提到的 `ext_model` 和 `cls_model`，重命名后放入相应的目录中。 + +**（3）高性能预测** +如果你想将本项目部署到线上环境去运行，那么建议你使用本项目基于 Paddle Inference 实现的高性能推理脚本 `deploy/predict.py`。 + +在使用之前，首先需要将保存的动态图模型转为静态图，通过调用下面的命令，便可将评论观点抽取模型和属性级情感分类模型转为静态图模型： +```shell +sh run_export_model.sh extraction +sh run_export_model.sh classification +``` + +这里需要注意的是，要确保相应的动态图已经下载或者训练生成到 `model_path` 指定的目录中，静态图模型会自动生成到`save_path`指定的地址。 + +同上，高性能预测的默认输入和输出形式也为文件，可分别通过 `test_path` 和 `save_path` 进行指定，通过如下命令便可以基于Paddle Inference 进行高性能预测： +```shell +cd deploy +sh run_predict.sh +``` + +**（4）自定义模型训练** +如果你希望自己尝试进行评论观点抽取模型训练，可使用4.1节中提供的 `ext_data` Demo 数据，或自己业务的标注数据重新训练模型，本项目已将评论观点抽取模型的相关训练和测试代码放入 `extraction` 目录下， 请到该目录下执行模型训练即可，更多的实现细节和和使用方式，请参考[这里](extraction/README.md)。 + +如果你希望自己尝试进行属性级情感分类模型训练，可使用4.1节中提供的 `cls_data` Demo 数据，或自己业务的标注数据重新训练模型，本项目已将属性级情感分类模型的相关训练和测试代码放入 `classification` 目录下，请到该目录下执行模型训练即可，更多的实现细节和使用方式，请参考[这里](classification/README.md)。 + +在训练后，如果需要进行高性能预测，可参考（3）进行动转静，然后基于Paddle Inference 进行高性能预测。 + +### 4.3 数据标注说明 +如果你想标注自己的业务数据，并尝试利用标注的新数据重新训练本项目。本项目推荐使用 [doccano](https://github.com/doccano/doccano) 进行数据标注平台，同时本项目也打通了其从标注到训练的通道，即 doccano 导出的数据后可通过 [doccano.py](./doccano.py) 脚本轻松将数据转换为输入模型时需要的形式，实现无缝衔接。 为达到这个目的，您需要按以下标注规则在 doccano 平台上标注数据： + +
+ +

图2 数据标注样例图

+

+ +- 在doccano平台上，定义标签 Pos-Aspect、 Neg-Aspect 和 Opinion，其中 Pos-Aspect 表示 Aspect 的情感极性为正向；Neg-Aspect 表示 Aspect 的情感极性为负向；Opinion 表示相应的观点词。 +- 使用以上定义的标签开始标注数据，图2展示了一个标注样例。 +- 当标注完成后，在 doccano 平台上导出 `jsonl` 形式的文件，并将其重命名为 `doccano.json` 后，放入 `./data` 目录下。 +- 通过 [doccano.py](./doccano.py) 脚本进行数据形式转换，然后便可以开始进行相应模型训练。 + +```shell +python doccano.py \ + --doccano_file ./data/doccano.json \ + --save_ext_dir ./data/ext_data \ + --save_cls_dir ./data/cls_data +``` + +**备注：** +- 默认情况下 [doccano.py](./doccano.py) 脚本会按照比例将数据划分为 train/dev/test 数据集 +- 每次执行 [doccano.py](./doccano.py) 脚本，将会覆盖已有的同名数据文件 + +## 5. 小模型优化策略 +以上实验中，无论是评论观点抽取模型，还是属性级情感分类模型，使用的均是 Large 版的 SKEP 模型，考虑到企业用户在线上部署时会考虑到模型预测效率，本项目提供了一套基于 [PP-MiniLM](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/model_compression/pp-minilm) 中文特色小模型的解决方案。PP-MiniLM 提供了一套完整的小模型优化方案：首先使用 Task-agnostic 的方式进行模型蒸馏、然后依托于 [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) 进行模型裁剪、模型量化等模型压缩技术，有效减小了模型的规模，加快了模型运行速度。 + +本项目基于 PP-MiniLM 中文特色小模型进行 fine-tune 属性级情感分类模型，然后使用 PaddleSlim 对训练好的模型进行量化操作。 + +在实验进行后，我们将 SKEP-Large、PP-MiniLM、量化PP-MiniLM 三个模型在性能和效果方面进行了对比，如下表所示。可以看到，三者在本任务数据集上的评估指标几乎相等，但是 PP-MiniLM 小模型运行速度较 SKEP-Large 提高了4倍，量化后的 PP-MiniLM 运行速度较 SKEP-Large 提高了近8倍。更多的详细信息请参考[这里](./pp_minilm/README.md)。 + +|Model|运行时间(s)|precision|Recall|F1| +| ------------ | ------------ | ------------ |-----------|------------ | +|SKEP-Large|1.00x|0.98497|0.99139|0.98817| +|PP-MiniLM|4.95x|0.98379|0.98859|0.98618| +|量化 PP-MiniLM|8.93x|0.98312|0.98953|0.98631| + +## 6. 引用 + +[1] H. Tian et al., “SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis,” arXiv:2005.05635 [cs], May 2020, Accessed: Nov. 11, 2021. diff --git a/applications/sentiment_analysis/ASO_analysis/classification/README.md b/applications/sentiment_analysis/ASO_analysis/classification/README.md new file mode 100644 index 000000000000..3d7bbc5c719c --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/classification/README.md @@ -0,0 +1,64 @@ +# 细粒度情感分类模型 + +## 1. 方案设计 + +本项目将进行属性级别的情感分类，对于给定的一段文本，我们在基于评论观点抽取模型抽取出不同属性对应的观点后，便可以有针对性地对各个属性判别情感极性。具体来讲，本项目将抽取出的评论属性和观点进行拼接，然后和原始语句进行拼接作为一条独立的训练语句。 + +如图1所示，首先将评论属性和观点词进行拼接为"味道好"，然后将"味道好"和原文进行拼接，然后传入SKEP模型，并使用 "CLS" 位置的向量进行细粒度情感倾向。 + +
+ +

图1 细粒度情感分类模型

+

+ +## 2. 项目结构说明 + +以下是本项目运行的完整目录结构及说明： + +```shell +. +├── data.py # 数据处理脚本 +├── model.py # 模型组网脚本 +├── train.py # 模型训练脚本 +├── evaluate.py # 模型评估脚本 +├── run_train.sh # 模型训练命令 +├── run_evaluate.sh # 模型评估命令 +└── README.md +``` + +## 3. 数据说明 + +本实验中，相应的数据集需要包含3列数据：标签、评论观点和原文，下面给出了一些样本示例。 + +- 1 口味清淡 口味很清淡，价格也比较公道 +- 1 经济实惠 经济实惠，环境好，套餐划算 +- 0 设施一般 房间大，设施一般 + +可点击 [cls_data](https://bj.bcebos.com/v1/paddlenlp/data/cls_data.tar.gz) 进行 Demo 数据下载，将数据解压之后放入父目录的 `data/cls_data/` 文件夹下。 + +## 4. 模型效果展示 + +在分类模型训练过程中，总共训练了10轮，并选择了评估 F1 得分最高的 best 模型，下表展示了训练过程中使用的训练参数。我们同时开源了相应的模型，可点击下表的 `cls_model` 进行下载，下载后将模型重命名为 `best.pdparams`，然后放入父目录的 `checkpoints/cls_checkpoints` 中。 +|Model|训练参数配置|MD5| +| ------------ | ------------ |-----------| +|[cls_model](https://bj.bcebos.com/paddlenlp/models/best_cls.pdparams)|
learning_rate: 3e-5, batch_size: 16, max_seq_len:256, epochs：10
|3de6ddf581e665d9b1d035c29b49778a| + +我们基于训练过程中的 best 模型在验证集 `dev` 和测试集 `test` 上进行了评估测试，模型效果如下表所示: +|Model|数据集|precision|Recall|F1| +| ------------ | ------------ | ------------ |-----------|------------ | +|SKEP-Large|dev|0.98758|0.99251|0.99004| +|SKEP-Large|test|0.98497|0.99139|0.98817| + +**备注**： 以上数据是基于全量数据训练和测试结果，并非 Demo 数据集。 + +## 5. 模型训练 +通过运行以下命令进行分类模型训练： +```shell +sh run_train.sh +``` + +## 6. 模型测试 +通过运行以下命令进行分类模型测试： +```shell +sh run_evaluate.sh +``` diff --git a/applications/sentiment_analysis/ASO_analysis/classification/data.py b/applications/sentiment_analysis/ASO_analysis/classification/data.py new file mode 100644 index 000000000000..ee22abb9fb1e --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/classification/data.py @@ -0,0 +1,58 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +from tqdm import tqdm + + +def load_dict(dict_path): + with open(dict_path, "r", encoding="utf-8") as f: + words = [word.strip() for word in f.readlines()] + word2id = dict(zip(words, range(len(words)))) + id2word = dict((v, k) for k, v in word2id.items()) + + return word2id, id2word + + +def read(data_path): + with open(data_path, "r", encoding="utf-8") as f: + for line in f.readlines(): + items = line.strip().split("\t") + assert len(items) == 3 + example = { + "label": int(items[0]), + "aspect_text": items[1], + "text": items[2] + } + + yield example + + +def convert_example_to_feature(example, + tokenizer, + label2id, + max_seq_len=512, + is_test=False): + encoded_inputs = tokenizer(example["aspect_text"], + text_pair=example["text"], + max_seq_len=max_seq_len, + return_length=True) + + if not is_test: + label = example["label"] + return encoded_inputs["input_ids"], encoded_inputs[ + "token_type_ids"], encoded_inputs["seq_len"], label + + return encoded_inputs["input_ids"], encoded_inputs[ + "token_type_ids"], encoded_inputs["seq_len"] diff --git a/applications/sentiment_analysis/ASO_analysis/classification/evaluate.py b/applications/sentiment_analysis/ASO_analysis/classification/evaluate.py new file mode 100644 index 000000000000..650a7990cb73 --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/classification/evaluate.py @@ -0,0 +1,80 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +from tqdm import tqdm +from functools import partial +import paddle +from paddlenlp.data import Pad, Stack, Tuple +from paddlenlp.metrics.glue import AccuracyAndF1 +from paddlenlp.datasets import load_dataset +from paddlenlp.transformers import SkepForSequenceClassification, SkepTokenizer +from data import read, load_dict, convert_example_to_feature + + +def evaluate(model, data_loader, metric): + + model.eval() + metric.reset() + for batch_data in tqdm(data_loader): + input_ids, token_type_ids, _, labels = batch_data + logits = model(input_ids, token_type_ids=token_type_ids) + correct = metric.compute(logits, labels) + metric.update(correct) + + accuracy, precision, recall, f1, _ = metric.accumulate() + + return accuracy, precision, recall, f1 + + +if __name__ == "__main__": + # yapf: disable + parser = argparse.ArgumentParser() + parser.add_argument("--model_path", type=str, default=None, help="The path of saved model that you want to load.") + parser.add_argument('--test_path', type=str, default=None, help="The path of test set.") + parser.add_argument("--label_path", type=str, default=None, help="The path of label dict.") + parser.add_argument("--batch_size", type=int, default=32, help="Batch size per GPU/CPU for training.") + parser.add_argument("--max_seq_len", type=int, default=512, help="The maximum total input sequence length after tokenization.") + args = parser.parse_args() + # yapf: enbale + + # load dev data + model_name = "skep_ernie_1.0_large_ch" + label2id, id2label = load_dict(args.label_path) + test_ds = load_dataset(read, data_path=args.test_path, lazy=False) + + tokenizer = SkepTokenizer.from_pretrained(model_name) + trans_func = partial(convert_example_to_feature, tokenizer=tokenizer, label2id=label2id, max_seq_len=args.max_seq_len) + test_ds = test_ds.map(trans_func, lazy=False) + + batchify_fn = lambda samples, fn=Tuple( + Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"), + Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"), + Stack(dtype="int64"), + Stack(dtype="int64") + ): fn(samples) + + test_batch_sampler = paddle.io.BatchSampler(test_ds, batch_size=args.batch_size, shuffle=False) + test_loader = paddle.io.DataLoader(test_ds, batch_sampler=test_batch_sampler, collate_fn=batchify_fn) + + # load model + loaded_state_dict = paddle.load(args.model_path) + model = SkepForSequenceClassification.from_pretrained(model_name, num_classes=len(label2id)) + model.load_dict(loaded_state_dict) + + metric = AccuracyAndF1() + + # evalute on dev data + accuracy, precision, recall, f1 = evaluate(model, test_loader, metric) + print(f'evalution result: accuracy:{accuracy:.5f} precision: {precision:.5f}, recall: {recall:.5f}, F1: {f1:.5f}') diff --git a/applications/sentiment_analysis/ASO_analysis/classification/run_evaluate.sh b/applications/sentiment_analysis/ASO_analysis/classification/run_evaluate.sh new file mode 100644 index 000000000000..dd266862e06b --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/classification/run_evaluate.sh @@ -0,0 +1,8 @@ +export CUDA_VISIBLE_DEVICES=0 + +python evaluate.py \ + --model_path "../checkpoints/cls_checkpoints/best.pdparams" \ + --test_path "../data/cls_data/test.txt" \ + --label_path "../data/cls_data/label.dict" \ + --batch_size 16 \ + --max_seq_len 256 diff --git a/applications/sentiment_analysis/ASO_analysis/classification/run_train.sh b/applications/sentiment_analysis/ASO_analysis/classification/run_train.sh new file mode 100644 index 000000000000..a0b54a7e1a43 --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/classification/run_train.sh @@ -0,0 +1,18 @@ +export CUDA_VISIBLE_DEVICES=0 + +python train.py \ + --train_path "../data/cls_data/train.txt" \ + --dev_path "../data/cls_data/dev.txt" \ + --label_path "../data/cls_data/label.dict" \ + --num_epochs 5 \ + --batch_size 16 \ + --max_seq_len 256 \ + --learning_rate 3e-5 \ + --weight_decay 0.01 \ + --max_grad_norm 1.0 \ + --warmup_proportion 0.1 \ + --log_steps 50 \ + --eval_steps 100 \ + --seed 1000 \ + --device "gpu" \ + --checkpoints "../checkpoints/cls_checkpoints" diff --git a/applications/sentiment_analysis/ASO_analysis/classification/train.py b/applications/sentiment_analysis/ASO_analysis/classification/train.py new file mode 100644 index 000000000000..c05dffa67e82 --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/classification/train.py @@ -0,0 +1,161 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys + +sys.path.append("../") + +import os +import argparse +import warnings +from functools import partial +import paddle +import paddle.nn.functional as F +from paddlenlp.metrics.glue import AccuracyAndF1 +from paddlenlp.datasets import load_dataset +from paddlenlp.data import Pad, Stack, Tuple +from paddlenlp.transformers import SkepTokenizer, SkepForSequenceClassification, LinearDecayWithWarmup +from evaluate import evaluate +from utils import set_seed +from data import read, load_dict, convert_example_to_feature + +warnings.filterwarnings("ignore") + + +def train(): + # set running envir + model_name = "skep_ernie_1.0_large_ch" + + paddle.set_device(args.device) + set_seed(args.seed) + + if not os.path.exists(args.checkpoints): + os.mkdir(args.checkpoints) + + # load and process data + label2id, id2label = load_dict(args.label_path) + train_ds = load_dataset(read, data_path=args.train_path, lazy=False) + dev_ds = load_dataset(read, data_path=args.dev_path, lazy=False) + + tokenizer = SkepTokenizer.from_pretrained(model_name) + trans_func = partial(convert_example_to_feature, + tokenizer=tokenizer, + label2id=label2id, + max_seq_len=args.max_seq_len) + train_ds = train_ds.map(trans_func, lazy=False) + dev_ds = dev_ds.map(trans_func, lazy=False) + + batchify_fn = lambda samples, fn=Tuple( + Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"), + Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"), + Stack(dtype="int64"), Stack(dtype="int64")): fn(samples) + + train_batch_sampler = paddle.io.BatchSampler(train_ds, + batch_size=args.batch_size, + shuffle=True) + dev_batch_sampler = paddle.io.BatchSampler(dev_ds, + batch_size=args.batch_size, + shuffle=False) + train_loader = paddle.io.DataLoader(train_ds, + batch_sampler=train_batch_sampler, + collate_fn=batchify_fn) + dev_loader = paddle.io.DataLoader(dev_ds, + batch_sampler=dev_batch_sampler, + collate_fn=batchify_fn) + + # configure model training + model = SkepForSequenceClassification.from_pretrained( + model_name, num_classes=len(label2id)) + + num_training_steps = len(train_loader) * args.num_epochs + lr_scheduler = LinearDecayWithWarmup(learning_rate=args.learning_rate, + total_steps=num_training_steps, + warmup=args.warmup_proportion) + decay_params = [ + p.name for n, p in model.named_parameters() + if not any(nd in n for nd in ["bias", "norm"]) + ] + grad_clip = paddle.nn.ClipGradByGlobalNorm(args.max_grad_norm) + optimizer = paddle.optimizer.AdamW( + learning_rate=lr_scheduler, + parameters=model.parameters(), + weight_decay=args.weight_decay, + apply_decay_param_fun=lambda x: x in decay_params, + grad_clip=grad_clip) + + metric = AccuracyAndF1() + + # start to train model + global_step, best_f1 = 1, 0. + model.train() + for epoch in range(1, args.num_epochs + 1): + for batch_data in train_loader(): + input_ids, token_type_ids, _, labels = batch_data + # logits: batch_size, seql_len, num_tags + logits = model(input_ids, token_type_ids=token_type_ids) + loss = F.cross_entropy(logits, labels) + + loss.backward() + lr_scheduler.step() + optimizer.step() + optimizer.clear_grad() + + if global_step > 0 and global_step % args.log_steps == 0: + print( + f"epoch: {epoch} - global_step: {global_step}/{num_training_steps} - loss:{loss.numpy().item():.6f}" + ) + if (global_step > 0 and global_step % args.eval_steps + == 0) or global_step == num_training_steps: + accuracy, precision, recall, f1 = evaluate( + model, dev_loader, metric) + model.train() + if f1 > best_f1: + print( + f"best F1 performence has been updated: {best_f1:.5f} --> {f1:.5f}" + ) + best_f1 = f1 + paddle.save(model.state_dict(), + f"{args.checkpoints}/best.pdparams") + print( + f'evalution result: accuracy:{accuracy:.5f} precision: {precision:.5f}, recall: {recall:.5f}, F1: {f1:.5f}' + ) + + global_step += 1 + + paddle.save(model.state_dict(), f"{args.checkpoints}/final.pdparams") + + +if __name__ == "__main__": + # yapf: disable + parser = argparse.ArgumentParser(__doc__) + parser.add_argument("--num_epochs", type=int, default=3, help="Number of epoches for training.") + parser.add_argument("--train_path", type=str, default=None, help="The path of train set.") + parser.add_argument("--dev_path", type=str, default=None, help="The path of dev set.") + parser.add_argument("--label_path", type=str, default=None, help="The path of label dict.") + parser.add_argument("--batch_size", type=int, default=32, help="Batch size per GPU/CPU for training.") + parser.add_argument("--max_seq_len", type=int, default=512, help="The maximum total input sequence length after tokenization.") + parser.add_argument("--learning_rate", type=float, default=5e-5, help="The initial learning rate for optimizer.") + parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.") + parser.add_argument("--max_grad_norm", type=float, default=1.0, help="Max grad norm to clip gradient.") + parser.add_argument("--warmup_proportion", type=float, default=0.1, help="Linear warmup proption over the training process.") + parser.add_argument("--log_steps", type=int, default=50, help="Frequency of printing log.") + parser.add_argument("--eval_steps", type=int, default=500, help="Frequency of performing evaluation.") + parser.add_argument("--seed", type=int, default=1000, help="Random seed for initialization.") + parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.") + parser.add_argument("--checkpoints", type=str, default=None, help="Directory to save checkpoint.") + + args = parser.parse_args() + # yapf: enable + + train() diff --git a/applications/sentiment_analysis/ASO_analysis/demo.py b/applications/sentiment_analysis/ASO_analysis/demo.py new file mode 100644 index 000000000000..232fa1bd389c --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/demo.py @@ -0,0 +1,136 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import paddle +from paddlenlp.transformers import SkepTokenizer, SkepForTokenClassification, SkepForSequenceClassification +from utils import decoding, load_dict +from seqeval.metrics.sequence_labeling import get_entities + + +def is_aspect_first(text, aspect, opinion_word): + return text.find(aspect) <= text.find(opinion_word) + + +def concate_aspect_and_opinion(text, aspect, opinion_words): + aspect_text = "" + for opinion_word in opinion_words: + if is_aspect_first(text, aspect, opinion_word): + aspect_text += aspect + opinion_word + "，" + else: + aspect_text += opinion_word + aspect + "，" + aspect_text = aspect_text[:-1] + + return aspect_text + + +def format_print(results): + for result in results: + aspect, opinions, sentiment = result["aspect"], result[ + "opinions"], result["sentiment_polarity"] + print( + f"aspect: {aspect}, opinions: {opinions}, sentiment_polarity: {sentiment}" + ) + print() + + +def predict(args, ext_model, cls_model, tokenizer, ext_id2label, cls_id2label): + + ext_model.eval() + cls_model.eval() + + while True: + input_text = input("input text: \n") + if not input_text: + continue + if input_text == "quit": + break + + input_text = input_text.strip().replace(" ", "") + # processing input text + encoded_inputs = tokenizer(list(input_text), + is_split_into_words=True, + max_seq_len=args.ext_max_seq_len) + input_ids = paddle.to_tensor([encoded_inputs["input_ids"]]) + token_type_ids = paddle.to_tensor([encoded_inputs["token_type_ids"]]) + + # extract aspect and opinion words + logits = ext_model(input_ids, token_type_ids=token_type_ids) + predictions = logits.argmax(axis=2).numpy()[0] + tag_seq = [ext_id2label[idx] for idx in predictions][1:-1] + + aps = decoding(input_text[:args.ext_max_seq_len - 2], tag_seq) + + # predict sentiment for aspect with cls_model + results = [] + for ap in aps: + aspect = ap[0] + opinion_words = list(set(ap[1:])) + aspect_text = concate_aspect_and_opinion(input_text, aspect, + opinion_words) + + encoded_inputs = tokenizer(aspect_text, + text_pair=input_text, + max_seq_len=args.cls_max_seq_len, + return_length=True) + input_ids = paddle.to_tensor([encoded_inputs["input_ids"]]) + token_type_ids = paddle.to_tensor( + [encoded_inputs["token_type_ids"]]) + + logits = cls_model(input_ids, token_type_ids=token_type_ids) + prediction = logits.argmax(axis=1).numpy()[0] + + result = { + "aspect": aspect, + "opinions": opinion_words, + "sentiment_polarity": cls_id2label[prediction] + } + results.append(result) + + format_print(results) + + +if __name__ == "__main__": + # yapf: disable + parser = argparse.ArgumentParser() + parser.add_argument("--ext_model_path", type=str, default=None, help="The path of extraction model path that you want to load.") + parser.add_argument("--cls_model_path", type=str, default=None, help="The path of classification model path that you want to load.") + parser.add_argument("--ext_label_path", type=str, default=None, help="The path of extraction label dict.") + parser.add_argument("--cls_label_path", type=str, default=None, help="The path of classification label dict.") + parser.add_argument("--ext_max_seq_len", type=int, default=512, help="The maximum total input sequence length after tokenization for extraction model.") + parser.add_argument("--cls_max_seq_len", type=int, default=512, help="The maximum total input sequence length after tokenization for classification model.") + args = parser.parse_args() + # yapf: enbale + + # load dict + model_name = "skep_ernie_1.0_large_ch" + ext_label2id, ext_id2label = load_dict(args.ext_label_path) + cls_label2id, cls_id2label = load_dict(args.cls_label_path) + tokenizer = SkepTokenizer.from_pretrained(model_name) + print("label dict loaded.") + + # load ext model + ext_state_dict = paddle.load(args.ext_model_path) + ext_model = SkepForTokenClassification.from_pretrained(model_name, num_classes=len(ext_label2id)) + ext_model.load_dict(ext_state_dict) + print("extraction model loaded.") + + # load cls model + cls_state_dict = paddle.load(args.cls_model_path) + cls_model = SkepForSequenceClassification.from_pretrained(model_name, num_classes=len(cls_label2id)) + cls_model.load_dict(cls_state_dict) + print("classification model loaded.") + + # do predict + predict(args, ext_model, cls_model, tokenizer, ext_id2label, cls_id2label) diff --git a/applications/sentiment_analysis/ASO_analysis/deploy/predict.py b/applications/sentiment_analysis/ASO_analysis/deploy/predict.py new file mode 100644 index 000000000000..cd8778f7be50 --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/deploy/predict.py @@ -0,0 +1,252 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys + +sys.path.append("../") + +import os +import json +import copy +import argparse +import numpy as np +from functools import partial +from collections import defaultdict +import paddle +from paddle import inference +from paddlenlp.datasets import load_dataset, MapDataset +from paddlenlp.data import Stack, Tuple, Pad +from paddlenlp.transformers import SkepTokenizer +from utils import decoding, read_test_file, load_dict +from extraction.data import convert_example_to_feature as convert_example_to_feature_ext +from classification.data import convert_example_to_feature as convert_example_to_feature_cls + + +class Predictor(object): + + def __init__(self, args): + self.args = args + self.ext_predictor, self.ext_input_handles, self.ext_output_hanle = self.create_predictor( + args.ext_model_path) + print(f"ext_model_path: {args.ext_model_path}, {self.ext_predictor}") + self.cls_predictor, self.cls_input_handles, self.cls_output_hanle = self.create_predictor( + args.cls_model_path) + self.ext_label2id, self.ext_id2label = load_dict(args.ext_label_path) + self.cls_label2id, self.cls_id2label = load_dict(args.cls_label_path) + self.tokenizer = SkepTokenizer.from_pretrained(args.base_model_name) + + def create_predictor(self, model_path): + model_file = model_path + ".pdmodel" + params_file = model_path + ".pdiparams" + if not os.path.exists(model_file): + raise ValueError("not find model file path {}".format(model_file)) + if not os.path.exists(params_file): + raise ValueError("not find params file path {}".format(params_file)) + config = paddle.inference.Config(model_file, params_file) + + if self.args.device == "gpu": + # set GPU configs accordingly + # such as intialize the gpu memory, enable tensorrt + config.enable_use_gpu(100, 0) + precision_map = { + "fp16": inference.PrecisionType.Half, + "fp32": inference.PrecisionType.Float32, + "int8": inference.PrecisionType.Int8 + } + precision_mode = precision_map[args.precision] + + if args.use_tensorrt: + config.enable_tensorrt_engine( + max_batch_size=self.args.batch_size, + min_subgraph_size=30, + precision_mode=precision_mode) + elif self.args.device == "cpu": + # set CPU configs accordingly, + # such as enable_mkldnn, set_cpu_math_library_num_threads + config.disable_gpu() + if args.enable_mkldnn: + # cache 10 different shapes for mkldnn to avoid memory leak + config.set_mkldnn_cache_capacity(10) + config.enable_mkldnn() + config.set_cpu_math_library_num_threads(args.cpu_threads) + elif self.args.device == "xpu": + # set XPU configs accordingly + config.enable_xpu(100) + + config.switch_use_feed_fetch_ops(False) + predictor = paddle.inference.create_predictor(config) + input_handles = [ + predictor.get_input_handle(name) + for name in predictor.get_input_names() + ] + output_handle = predictor.get_output_handle( + predictor.get_output_names()[0]) + + return predictor, input_handles, output_handle + + def predict_ext(self, args): + ori_test_ds = load_dataset(read_test_file, + data_path=args.test_path, + lazy=False) + trans_func = partial(convert_example_to_feature_ext, + tokenizer=self.tokenizer, + label2id=self.ext_label2id, + max_seq_len=args.ext_max_seq_len, + is_test=True) + test_ds = copy.copy(ori_test_ds).map(trans_func, lazy=False) + batch_list = [ + test_ds[idx:idx + args.batch_size] + for idx in range(0, len(test_ds), args.batch_size) + ] + + batchify_fn = lambda samples, fn=Tuple( + Pad(axis=0, pad_val=self.tokenizer.pad_token_id, dtype="int64"), + Pad(axis=0, pad_val=self.tokenizer.pad_token_type_id, dtype="int64" + ), Stack(dtype="int64")): fn(samples) + + results = [] + for bid, batch_data in enumerate(batch_list): + input_ids, token_type_ids, seq_lens = batchify_fn(batch_data) + self.ext_input_handles[0].copy_from_cpu(input_ids) + self.ext_input_handles[1].copy_from_cpu(token_type_ids) + self.ext_predictor.run() + logits = self.ext_output_hanle.copy_to_cpu() + + predictions = logits.argmax(axis=2) + for eid, (seq_len, + prediction) in enumerate(zip(seq_lens, predictions)): + idx = bid * args.batch_size + eid + tag_seq = [ + self.ext_id2label[idx] for idx in prediction[:seq_len][1:-1] + ] + text = ori_test_ds[idx]["text"] + aps = decoding(text[:args.ext_max_seq_len - 2], tag_seq) + for aid, ap in enumerate(aps): + aspect, opinions = ap[0], list(set(ap[1:])) + aspect_text = self._concate_aspect_and_opinion( + text, aspect, opinions) + results.append({ + "id": str(idx) + "_" + str(aid), + "aspect": aspect, + "opinions": opinions, + "text": text, + "aspect_text": aspect_text + }) + return results + + def predict_cls(self, args, ext_results): + test_ds = MapDataset(ext_results) + trans_func = partial(convert_example_to_feature_cls, + tokenizer=self.tokenizer, + label2id=self.cls_label2id, + max_seq_len=args.cls_max_seq_len, + is_test=True) + test_ds = test_ds.map(trans_func, lazy=False) + batch_list = [ + test_ds[idx:idx + args.batch_size] + for idx in range(0, len(test_ds), args.batch_size) + ] + + batchify_fn = lambda samples, fn=Tuple( + Pad(axis=0, pad_val=self.tokenizer.pad_token_id, dtype="int64"), + Pad(axis=0, pad_val=self.tokenizer.pad_token_type_id, dtype="int64" + ), Stack(dtype="int64")): fn(samples) + + results = [] + for batch_data in batch_list: + input_ids, token_type_ids, _ = batchify_fn(batch_data) + self.cls_input_handles[0].copy_from_cpu(input_ids) + self.cls_input_handles[1].copy_from_cpu(token_type_ids) + self.cls_predictor.run() + logits = self.cls_output_hanle.copy_to_cpu() + + predictions = logits.argmax(axis=1).tolist() + results.extend(predictions) + + return results + + def post_process(self, args, ext_results, cls_results): + assert len(ext_results) == len(cls_results) + + collect_dict = defaultdict(list) + for ext_result, cls_result in zip(ext_results, cls_results): + ext_result["sentiment_polarity"] = self.cls_id2label[cls_result] + eid, _ = ext_result["id"].split("_") + collect_dict[eid].append(ext_result) + + sentiment_results = [] + for eid in collect_dict.keys(): + sentiment_result = {} + ap_list = [] + for idx, single_ap in enumerate(collect_dict[eid]): + if idx == 0: + sentiment_result["text"] = single_ap["text"] + ap_list.append({ + "aspect": + single_ap["aspect"], + "opinions": + single_ap["opinions"], + "sentiment_polarity": + single_ap["sentiment_polarity"] + }) + sentiment_result["ap_list"] = ap_list + sentiment_results.append(sentiment_result) + + with open(args.save_path, "w", encoding="utf-8") as f: + for sentiment_result in sentiment_results: + f.write(json.dumps(sentiment_result, ensure_ascii=False) + "\n") + print( + f"sentiment analysis results has been saved to path: {args.save_path}" + ) + + def predict(self, args): + ext_results = self.predict_ext(args) + cls_results = self.predict_cls(args, ext_results) + self.post_process(args, ext_results, cls_results) + + def _concate_aspect_and_opinion(self, text, aspect, opinion_words): + aspect_text = "" + for opinion_word in opinion_words: + if text.find(aspect) <= text.find(opinion_word): + aspect_text += aspect + opinion_word + "，" + else: + aspect_text += opinion_word + aspect + "，" + aspect_text = aspect_text[:-1] + + return aspect_text + + +if __name__ == "__main__": + # yapf: disable + parser = argparse.ArgumentParser() + parser.add_argument("--base_model_name", default='skep_ernie_1.0_large_ch', type=str, help="Base model name, SKEP used by default", ) + parser.add_argument("--ext_model_path", type=str, default=None, help="The path of extraction model path that you want to load.") + parser.add_argument("--cls_model_path", type=str, default=None, help="The path of classification model path that you want to load.") + parser.add_argument("--ext_label_path", type=str, default=None, help="The path of extraction label dict.") + parser.add_argument("--cls_label_path", type=str, default=None, help="The path of classification label dict.") + parser.add_argument('--test_path', type=str, default=None, help="The path of test set that you want to predict.") + parser.add_argument('--save_path', type=str, required=True, default=None, help="The saving path of predict results.") + parser.add_argument("--batch_size", type=int, default=16, help="Batch size per GPU/CPU for training.") + parser.add_argument("--ext_max_seq_len", type=int, default=512, help="The maximum total input sequence length after tokenization for extraction model.") + parser.add_argument("--cls_max_seq_len", type=int, default=512, help="The maximum total input sequence length after tokenization for classification model.") + parser.add_argument("--use_tensorrt", action='store_true', help="Whether to use inference engin TensorRT.") + parser.add_argument("--precision", default="fp32", type=str, choices=["fp32", "fp16", "int8"],help='The tensorrt precision.') + parser.add_argument("--device", default="gpu", choices=["gpu", "cpu", "xpu"], help="Device selected for inference.") + parser.add_argument('--cpu_threads', default=10, type=int, help='Number of threads to predict when using cpu.') + parser.add_argument('--enable_mkldnn', default=False, type=eval, choices=[True, False], help='Enable to use mkldnn to speed up when using cpu.') + args = parser.parse_args() + # yapf: enbale + + predictor = Predictor(args) + predictor.predict(args) diff --git a/applications/sentiment_analysis/ASO_analysis/deploy/run_predict.sh b/applications/sentiment_analysis/ASO_analysis/deploy/run_predict.sh new file mode 100644 index 000000000000..8b373cbc9d44 --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/deploy/run_predict.sh @@ -0,0 +1,13 @@ +export CUDA_VISIBLE_DEVICES=0 + +python predict.py \ + --base_model_name "skep_ernie_1.0_large_ch" \ + --ext_model_path "../checkpoints/ext_checkpoints/static/infer" \ + --cls_model_path "../checkpoints/cls_checkpoints/static/infer" \ + --ext_label_path "../data/ext_data/label.dict" \ + --cls_label_path "../data/cls_data/label.dict" \ + --test_path "../data/test.txt" \ + --save_path "../data/sentiment_results.json" \ + --batch_size 8 \ + --ext_max_seq_len 512 \ + --cls_max_seq_len 256 diff --git a/applications/sentiment_analysis/ASO_analysis/doccano.py b/applications/sentiment_analysis/ASO_analysis/doccano.py new file mode 100644 index 000000000000..6f2fc5cc2f10 --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/doccano.py @@ -0,0 +1,166 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import json +import argparse +import numpy as np +from utils import decoding, concate_aspect_and_opinion, save_examples, save_dict + + +def doccano2SA(doccano_file, + save_ext_dir, + save_cls_dir, + splits=[0.8, 0.9], + is_shuffle=True): + """ + @Description: Consvert doccano file to data format which is suitable to input to this Application. + @Param doccano_file: The annotated file exported from doccano labeling platform. + @Param save_ext_dir: The directory of ext data that you wanna save. + @Param save_cls_dir: The directory of cls data that you wanna save. + @Param splits: Whether to split doccano file into train/dev/test, note: Only []/ len(splits)==2 accepted. + @Param is_shuffle: Whether to shuffle data. + """ + if not os.path.exists(doccano_file): + raise ValueError("Please input the correct path of doccano file.") + + if not os.path.exists(save_ext_dir): + os.makedirs(save_ext_dir) + + if not os.path.exists(save_cls_dir): + os.makedirs(save_cls_dir) + + if len(splits) != 0 and len(splits) != 2: + raise ValueError("Only []/ len(splits)==2 accepted for splits.") + + if splits and (splits[0] >= splits[1] or splits[0] >= 1.0 + or splits[1] >= 1.0 or splits[0] <= 0. or splits[1] <= 0): + raise ValueError( + "Please set correct splits, the element in it should be in (0,1), and splits[1]>splits[0]." + ) + + def label_ext_with_label_term(ext_label, start, end, tag): + + if tag == "Opinion": + b_tag = "B-Opinion" + i_tag = "I-Opinion" + else: + b_tag = "B-Aspect" + i_tag = "I-Aspect" + + ext_label[start] = b_tag + for i in range(start + 1, end): + ext_label[i] = i_tag + + ext_examples, cls_examples = [], [] + with open(doccano_file, "r", encoding="utf-8") as f: + raw_examples = f.readlines() + # start to label for ext and cls data + for line in raw_examples: + items = json.loads(line) + text, label_terms = items["data"], items["label"] + # label ext data with label_terms + ext_label = ["O"] * len(text) + aspect_mapper = {} + for label_term in label_terms: + start, end, tag = label_term + label_ext_with_label_term(ext_label, start, end, tag) + if tag == "Pos-Aspect": + aspect_mapper[text[start:end]] = "1" + elif tag == "Neg-Aspect": + aspect_mapper[text[start:end]] = "0" + ext_examples.append((text, " ".join(ext_label))) + # label cls data + aps = decoding(text, ext_label) + for ap in aps: + aspect, opinions = ap[0], list(set(ap[1:])) + if aspect not in aspect_mapper: + continue + aspect_text = concate_aspect_and_opinion(text, aspect, opinions) + cls_examples.append((aspect_mapper[aspect], aspect_text, text)) + + # index for saving data + ext_idx = np.arange(len(ext_examples)) + cls_idx = np.arange(len(cls_examples)) + + if is_shuffle: + ext_idx = np.random.permutation(ext_idx) + cls_idx = np.random.permutation(cls_idx) + + if len(splits) == 0: + # save ext data + save_ext_path = os.path.join(save_ext_dir, "doccano.txt") + save_examples(ext_examples, save_ext_path, ext_idx) + print(f"\next: save data to {save_ext_path}.") + # save cls data + save_cls_path = os.path.join(save_cls_dir, "doccano.txt") + save_examples(cls_examples, save_cls_path, cls_idx) + print(f"\ncls: save data to {save_cls_path}.") + + else: + # save ext data + eth1, eth2 = int(len(ext_examples) * splits[0]), int( + len(ext_examples) * splits[1]) + save_ext_train_path = os.path.join(save_ext_dir, "train.txt") + save_ext_dev_path = os.path.join(save_ext_dir, "dev.txt") + save_ext_test_path = os.path.join(save_ext_dir, "test.txt") + save_examples(ext_examples, save_ext_train_path, ext_idx[:eth1]) + save_examples(ext_examples, save_ext_dev_path, ext_idx[eth1:eth2]) + save_examples(ext_examples, save_ext_test_path, ext_idx[eth2:]) + print(f"\next: save train data to {save_ext_train_path}.") + print(f"ext: save dev data to {save_ext_dev_path}.") + print(f"ext: save test data to {save_ext_test_path}.") + + # save cls data + cth1, cth2 = int(len(cls_examples) * splits[0]), int( + len(cls_examples) * splits[1]) + save_cls_train_path = os.path.join(save_cls_dir, "train.txt") + save_cls_dev_path = os.path.join(save_cls_dir, "dev.txt") + save_cls_test_path = os.path.join(save_cls_dir, "test.txt") + save_examples(cls_examples, save_cls_train_path, cls_idx[:cth1]) + save_examples(cls_examples, save_cls_dev_path, cls_idx[cth1:cth2]) + save_examples(cls_examples, save_cls_test_path, cls_idx[cth2:]) + print(f"\ncls: save train data to {save_cls_train_path}.") + print(f"cls: save dev data to {save_cls_dev_path}.") + print(f"cls: save test data to {save_cls_test_path}.") + + # save ext dict + ext_dict_path = os.path.join(save_ext_dir, "label.dict") + cls_dict_path = os.path.join(save_cls_dir, "label.dict") + save_dict(ext_dict_path, "ext") + save_dict(cls_dict_path, "cls") + print(f"\next: save dict to {ext_dict_path}.") + print(f"cls: save dict to {cls_dict_path}.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--doccano_file", + type=str, + default="./data/doccano.json", + help="The doccano file exported from doccano platform.") + parser.add_argument("--save_ext_dir", + type=str, + default="./data/ext_data1", + help="The path of ext data that you wanna save.") + parser.add_argument("--save_cls_dir", + type=str, + default="./data/cls_data1", + help="The path of cls data that you wanna save.") + args = parser.parse_args() + + doccano2SA(args.doccano_file, + args.save_ext_dir, + args.save_cls_dir, + is_shuffle=True) diff --git a/applications/sentiment_analysis/ASO_analysis/export_model.py b/applications/sentiment_analysis/ASO_analysis/export_model.py new file mode 100644 index 000000000000..f22a32573d00 --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/export_model.py @@ -0,0 +1,55 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import argparse +import paddle +from paddlenlp.transformers import SkepForTokenClassification, SkepForSequenceClassification, PPMiniLMForSequenceClassification + +if __name__ == "__main__": + # yapf: disable + parser = argparse.ArgumentParser() + parser.add_argument("--model_type", type=str, default="extraction", choices=["extraction", "classification", "pp_minilm"], help="The model type that you wanna export.") + parser.add_argument("--base_model_name", type=str, default="skep_ernie_1.0_large_ch", help="The base model of experiment, skep or ppminilm") + parser.add_argument("--model_path", type=str, default=None, help="The path of model that you want to load.") + parser.add_argument("--save_path", type=str, default=None, help="The path of the exported static model.") + args = parser.parse_args() + # yapf: enbale + + # load model with saved state_dict + if args.model_type == "extraction": + model = SkepForTokenClassification.from_pretrained(args.base_model_name, num_classes=5) + elif args.model_type == "classification": + model = SkepForSequenceClassification.from_pretrained(args.base_model_name, num_classes=2) + else: + model = PPMiniLMForSequenceClassification.from_pretrained(args.base_model_name, num_classes=2) + + loaded_state_dict = paddle.load(args.model_path) + model.load_dict(loaded_state_dict) + print(f"Loaded parameters from {args.model_path}") + + model.eval() + # convert to static graph with specific input description + model = paddle.jit.to_static( + model, + input_spec=[ + paddle.static.InputSpec( + shape=[None, None], dtype="int64"), # input_ids + paddle.static.InputSpec( + shape=[None, None], dtype="int64") # token_type_ids + ]) + + # save to static model + paddle.jit.save(model, args.save_path) + print(f"static {args.model_type} model has been to {args.save_path}") diff --git a/applications/sentiment_analysis/ASO_analysis/extraction/README.md b/applications/sentiment_analysis/ASO_analysis/extraction/README.md new file mode 100644 index 000000000000..2a471a6bd62e --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/extraction/README.md @@ -0,0 +1,65 @@ +# 评论观点抽取模型 + +## 1. 方案设计 + +在本实验中，我们将采用序列标注的方式进行评论观点抽取，需要注意的是，这里会同时抽取评论的属性和观点，为此我们基于 BIO 的序列标注体系进行了标签的拓展：B-Aspect, I-Aspect, B-Opinion, I-Opinion, O，其中前两者用于标注评论属性，后两者用于标注评论观点。 + +如图1所示，首先将文本串传入 SKEP 模型中，利用 SKEP 模型对该文本串进行语义编码后，然后基于每个位置的输出去预测相应的标签。 + +
+ +

图1 评论观点抽取模型

+

+ +## 2. 项目结构说明 + +以下是本项目运行的完整目录结构及说明： + +```shell +. +├── data.py # 数据处理脚本 +├── model.py # 模型组网脚本 +├── train.py # 模型训练脚本 +├── evaluate.py # 模型评估脚本 +├── run_train.sh # 模型训练命令 +├── run_evaluate.sh # 模型评估命令 +└── README.md +``` + +## 3. 数据说明 + +如上所述，本项目将采用序列标注的方式进行抽取评论属性和观点，所以本项目训练集中需要包含两列数据：文本串和相应的序列标签数据，下面给出了一条样本。 + + +- 服务好，环境好，做出来效果也不错 B-Aspect I-Aspect B-Opinion O B-Aspect I-Aspect B-Opinion O O O O B-Aspect I-Aspect O B-Opinion I-Opinion +- 环境很好，交通便利 B-Aspect I-Aspect O B-Opinion O B-Aspect I-Aspect B-Opinion I-Opinion +- 空气清新，景色优美 B-Aspect I-Aspect B-Opinion I-Opinion O B-Aspect I-Aspect O B-Opinion + + +可点击 [ext_data](https://bj.bcebos.com/v1/paddlenlp/data/ext_data.tar.gz) 进行 Demo 数据下载，将数据解压之后放入父目录的 `data/ext_data/` 文件夹下。 + +## 4. 模型效果展示 +在抽取模型训练过程中，总共训练了10轮，并选择了评估F1得分最高的 best 模型，下表展示了训练过程中使用的训练参数。我们同时开源了相应的模型，可点击下表的 `ext_model` 进行下载，下载后将模型重命名为 `best.pdparams`，然后放入父目录的 `checkpoints/ext_checkpoints` 中。 +|Model|训练参数配置|MD5| +| ------------ | ------------ |-----------| +|[ext_model](https://bj.bcebos.com/paddlenlp/models/best_ext.pdparams)|
learning_rate: 5e-5, batch_size: 8, max_seq_len:512, epochs：10
|e3358632165aa0338225e175b57cb304| + +我们基于训练过程中的 best 模型在验证集 `dev` 和测试集 `test` 上进行了评估测试，模型效果如下表所示: +|Model|数据集|precision|Recall|F1| +| ------------ | ------------ | ------------ |-----------|------------ | +|SKEP-Large|dev|0.87095|0.90056|0.88551| +|SKEP-Large|test|0.87125|0.89944|0.88512| + +**备注**：以上数据是基于全量数据训练和测试结果，并非 Demo 数据集。 + +## 5. 模型训练 +通过运行以下命令进行评论观点抽取模型训练： +```shell +sh run_train.sh +``` + +## 6. 模型测试 +通过运行以下命令进行评论观点抽取模型测试： +```shell +sh run_evaluate.sh +``` diff --git a/applications/sentiment_analysis/ASO_analysis/extraction/data.py b/applications/sentiment_analysis/ASO_analysis/extraction/data.py new file mode 100644 index 000000000000..2f5bcee8f39a --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/extraction/data.py @@ -0,0 +1,65 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +from tqdm import tqdm +from collections import defaultdict + + +def load_dict(dict_path): + with open(dict_path, "r", encoding="utf-8") as f: + words = [word.strip() for word in f.readlines()] + word2id = dict(zip(words, range(len(words)))) + id2word = dict((v, k) for k, v in word2id.items()) + + return word2id, id2word + + +def read(data_path): + with open(data_path, "r", encoding="utf-8") as f: + for line in f.readlines(): + text, label = line.split("\t") + text = text[:-1] if text[-1] == "\n" else text + label = label[:-1] if label[-1] == "\n" else label + label = label.split(" ") + text = list(text) + assert len(text) == len(label), f"{text}, {label}" + example = {"text": text, "label": label} + + yield example + + +def convert_example_to_feature(example, + tokenizer, + label2id, + max_seq_len=512, + is_test=False): + encoded_inputs = tokenizer(list(example["text"]), + is_split_into_words=True, + max_seq_len=max_seq_len, + return_length=True) + + if not is_test: + label = [label2id["O"]] + [ + label2id[label_term] for label_term in example["label"] + ][:(max_seq_len - 2)] + [label2id["O"]] + + assert len(encoded_inputs["input_ids"]) == len( + label + ), f"input_ids: {len(encoded_inputs['input_ids'])}, label: {len(label)}" + return encoded_inputs["input_ids"], encoded_inputs[ + "token_type_ids"], encoded_inputs["seq_len"], label + + return encoded_inputs["input_ids"], encoded_inputs[ + "token_type_ids"], encoded_inputs["seq_len"] diff --git a/applications/sentiment_analysis/ASO_analysis/extraction/evaluate.py b/applications/sentiment_analysis/ASO_analysis/extraction/evaluate.py new file mode 100644 index 000000000000..3f91233d7f17 --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/extraction/evaluate.py @@ -0,0 +1,85 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +from tqdm import tqdm +from functools import partial +import paddle +from paddlenlp.data import Pad, Stack, Tuple +from paddlenlp.metrics import ChunkEvaluator +from paddlenlp.datasets import load_dataset +from paddlenlp.transformers import SkepForTokenClassification, SkepTokenizer +from data import read, load_dict, convert_example_to_feature + + +def evaluate(model, data_loader, metric): + + model.eval() + metric.reset() + for idx, batch_data in tqdm(enumerate(data_loader)): + input_ids, token_type_ids, seq_lens, labels = batch_data + logits = model(input_ids, token_type_ids=token_type_ids) + + # count metric + predictions = logits.argmax(axis=2) + num_infer_chunks, num_label_chunks, num_correct_chunks = metric.compute( + seq_lens, predictions, labels) + metric.update(num_infer_chunks.numpy(), num_label_chunks.numpy(), + num_correct_chunks.numpy()) + + precision, recall, f1 = metric.accumulate() + + return precision, recall, f1 + + +if __name__ == "__main__": + # yapf: disable + parser = argparse.ArgumentParser() + parser.add_argument("--model_path", type=str, default=None, help="The path of saved model that you want to load.") + parser.add_argument('--test_path', type=str, default=None, help="The path of test set.") + parser.add_argument("--label_path", type=str, default=None, help="The path of label dict.") + parser.add_argument("--batch_size", type=int, default=32, help="Batch size per GPU/CPU for training.") + parser.add_argument("--max_seq_len", type=int, default=512, help="The maximum total input sequence length after tokenization.") + args = parser.parse_args() + # yapf: enbale + + # load dev data + model_name = "skep_ernie_1.0_large_ch" + label2id, id2label = load_dict(args.label_path) + test_ds = load_dataset(read, data_path=args.test_path, lazy=False) + + tokenizer = SkepTokenizer.from_pretrained(model_name) + trans_func = partial(convert_example_to_feature, tokenizer=tokenizer, label2id=label2id, max_seq_len=args.max_seq_len) + test_ds = test_ds.map(trans_func, lazy=False) + + batchify_fn = lambda samples, fn=Tuple( + Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"), + Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"), + Stack(dtype="int64"), + Pad(axis=0, pad_val=-1, dtype="int64") + ): fn(samples) + + test_batch_sampler = paddle.io.BatchSampler(test_ds, batch_size=args.batch_size, shuffle=False) + test_loader = paddle.io.DataLoader(test_ds, batch_sampler=test_batch_sampler, collate_fn=batchify_fn) + + # load model + loaded_state_dict = paddle.load(args.model_path) + model = SkepForTokenClassification.from_pretrained(model_name, num_classes=len(label2id)) + model.load_dict(loaded_state_dict) + + metric = ChunkEvaluator(label2id.keys()) + + # evalute on dev data + precision, recall, f1 = evaluate(model, test_loader, metric) + print(f'evalution result: precision: {precision:.5f}, recall: {recall:.5f}, F1: {f1:.5f}') diff --git a/applications/sentiment_analysis/ASO_analysis/extraction/run_evaluate.sh b/applications/sentiment_analysis/ASO_analysis/extraction/run_evaluate.sh new file mode 100644 index 000000000000..5383969d1592 --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/extraction/run_evaluate.sh @@ -0,0 +1,9 @@ +export CUDA_VISIBLE_DEVICES=0 + +python evaluate.py \ + --model_path "../checkpoints/ext_checkpoints/best.pdparams" \ + --test_path "../data/ext_data/test.txt" \ + --label_path "../data/ext_data/label.dict" \ + --batch_size 16 \ + --max_seq_len 256 + diff --git a/applications/sentiment_analysis/ASO_analysis/extraction/run_train.sh b/applications/sentiment_analysis/ASO_analysis/extraction/run_train.sh new file mode 100644 index 000000000000..33258dc664b6 --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/extraction/run_train.sh @@ -0,0 +1,18 @@ +export CUDA_VISIBLE_DEVICES=0 + +python train.py \ + --train_path "../data/ext_data/train.txt" \ + --dev_path "../data/ext_data/dev.txt" \ + --label_path "../data/ext_data/label.dict" \ + --num_epochs 10 \ + --batch_size 16 \ + --max_seq_len 256 \ + --learning_rate 5e-5 \ + --weight_decay 0.01 \ + --max_grad_norm 1.0 \ + --warmup_proportion 0.1 \ + --log_steps 50 \ + --eval_steps 250 \ + --seed 1000 \ + --device "gpu" \ + --checkpoints "../checkpoints/ext_checkpoints/" diff --git a/applications/sentiment_analysis/ASO_analysis/extraction/train.py b/applications/sentiment_analysis/ASO_analysis/extraction/train.py new file mode 100644 index 000000000000..9b4e5d267002 --- /dev/null +++ b/applications/sentiment_analysis/ASO_analysis/extraction/train.py @@ -0,0 +1,164 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys + +sys.path.append("../") + +import os +import argparse +import warnings +from tqdm import tqdm +from functools import partial +import paddle +import paddle.nn.functional as F +from paddlenlp.metrics import ChunkEvaluator +from paddlenlp.datasets import load_dataset +from paddlenlp.data import Pad, Stack, Tuple +from paddlenlp.transformers import SkepTokenizer, SkepForTokenClassification, LinearDecayWithWarmup +from evaluate import evaluate +from utils import set_seed +from data import read, load_dict, convert_example_to_feature + +warnings.filterwarnings("ignore") + + +def train(): + # set running envir + model_name = "skep_ernie_1.0_large_ch" + + paddle.set_device(args.device) + set_seed(args.seed) + + if not os.path.exists(args.checkpoints): + os.mkdir(args.checkpoints) + + # load and process data + label2id, id2label = load_dict(args.label_path) + train_ds = load_dataset(read, data_path=args.train_path, lazy=False) + dev_ds = load_dataset(read, data_path=args.dev_path, lazy=False) + + tokenizer = SkepTokenizer.from_pretrained(model_name) + trans_func = partial(convert_example_to_feature, + tokenizer=tokenizer, + label2id=label2id, + max_seq_len=args.max_seq_len) + train_ds = train_ds.map(trans_func, lazy=False) + dev_ds = dev_ds.map(trans_func, lazy=False) + + batchify_fn = lambda samples, fn=Tuple( + Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"), + Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"), + Stack(dtype="int64"), Pad(axis=0, pad_val=-1, dtype="int64")): fn( + samples) + + train_batch_sampler = paddle.io.BatchSampler(train_ds, + batch_size=args.batch_size, + shuffle=True) + dev_batch_sampler = paddle.io.BatchSampler(dev_ds, + batch_size=args.batch_size, + shuffle=False) + train_loader = paddle.io.DataLoader(train_ds, + batch_sampler=train_batch_sampler, + collate_fn=batchify_fn) + dev_loader = paddle.io.DataLoader(dev_ds, + batch_sampler=dev_batch_sampler, + collate_fn=batchify_fn) + + # configure model training + model = SkepForTokenClassification.from_pretrained( + model_name, num_classes=len(label2id)) + + num_training_steps = len(train_loader) * args.num_epochs + lr_scheduler = LinearDecayWithWarmup(learning_rate=args.learning_rate, + total_steps=num_training_steps, + warmup=args.warmup_proportion) + decay_params = [ + p.name for n, p in model.named_parameters() + if not any(nd in n for nd in ["bias", "norm"]) + ] + grad_clip = paddle.nn.ClipGradByGlobalNorm(args.max_grad_norm) + optimizer = paddle.optimizer.AdamW( + learning_rate=lr_scheduler, + parameters=model.parameters(), + weight_decay=args.weight_decay, + apply_decay_param_fun=lambda x: x in decay_params, + grad_clip=grad_clip) + + metric = ChunkEvaluator(label2id.keys()) + + # start to train model + global_step, best_f1 = 1, 0. + model.train() + for epoch in range(1, args.num_epochs + 1): + for batch_data in train_loader(): + input_ids, token_type_ids, _, labels = batch_data + # logits: batch_size, seql_len, num_tags + logits = model(input_ids, token_type_ids=token_type_ids) + loss = F.cross_entropy(logits.reshape([-1, len(label2id)]), + labels.reshape([-1]), + ignore_index=-1) + + loss.backward() + lr_scheduler.step() + optimizer.step() + optimizer.clear_grad() + + if global_step > 0 and global_step % args.log_steps == 0: + print( + f"epoch: {epoch} - global_step: {global_step}/{num_training_steps} - loss:{loss.numpy().item():.6f}" + ) + if (global_step > 0 and global_step % args.eval_steps + == 0) or global_step == num_training_steps: + precision, recall, f1 = evaluate(model, dev_loader, metric) + model.train() + if f1 > best_f1: + print( + f"best F1 performence has been updated: {best_f1:.5f} --> {f1:.5f}" + ) + best_f1 = f1 + paddle.save(model.state_dict(), + f"{args.checkpoints}/best.pdparams") + print( + f'evalution result: precision: {precision:.5f}, recall: {recall:.5f}, F1: {f1:.5f}' + ) + + global_step += 1 + + paddle.save(model.state_dict(), f"{args.checkpoints}/final.pdparams") + + +if __name__ == "__main__": + # yapf: disable + parser = argparse.ArgumentParser(__doc__) + parser.add_argument("--num_epochs", type=int, default=3, help="Number of epoches for training.") + parser.add_argument("--train_path", type=str, default=None, help="The path of train set.") + parser.add_argument("--dev_path", type=str, default=None, help="The path of dev set.") + parser.add_argument("--label_path", type=str, default=None, help="The path of label dict.") + parser.add_argument("--batch_size", type=int, default=32, help="Total examples' number in batch for training.") + parser.add_argument("--max_seq_len", type=int, default=512, help="Batch size per GPU/CPU for training.") + parser.add_argument("--learning_rate", type=float, default=5e-5, help="The initial learning rate for optimizer.") + parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.") + parser.add_argument("--max_grad_norm", type=float, default=1.0, help="Max grad norm to clip gradient.") + parser.add_argument("--warmup_proportion", type=float, default=0.1, help="Linear warmup proption over the training process.") + parser.add_argument("--log_steps", type=int, default=50, help="Frequency of printing log.") + parser.add_argument("--eval_steps", type=int, default=500, help="Frequency of performing evaluation.") + parser.add_argument("--seed", type=int, default=1000, help="Random seed for initialization.") + parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.") + parser.add_argument("--checkpoints", type=str, default=None, help="Directory to save checkpoint.") + + args = parser.parse_args() + # yapf: enable + + train() diff --git a/applications/sentiment_analysis/ASO_analysis/imgs/design_cls_model.png b/applications/sentiment_analysis/ASO_analysis/imgs/design_cls_model.png new file mode 100644 index 0000000000000000000000000000000000000000..2aac511d38bc4aa6fcf82e05d3aa4c0725859744 GIT binary patch literal 738007 zcmb5WbzB=y)Hm9eQc8hRym)aB#T{DQou)t_Ee^p#km3c37bxyhTuN|A(Bck7g9LYX zD3Wsdz3=nfzwh18&Ys!%oY_4yduI2{neW+fZA~SD7gR4EJ$gi-qAaib=n*#Qqeo9{ zo5ejtxwx=ON-;3G6U4<}zNKdM-1XguP4$UlGd^fA>V?EhFElJsNh|2MDr znEesf|0#d+=uxQcqo@DFqxnGp+masAe|7#Z#mag7Kim&xIZytV8=Eu->woi4Z2qg3 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